Predictive virtual reality display system with post rendering correction

ABSTRACT

A virtual reality display system that generates display images in two phases: the first phase renders images based on a predicted pose at the time the display will be updated; the second phase re-predicts the pose using recent sensor data, and corrects the images based on changes since the initial prediction. The second phase may be delayed so that it occurs just in time for a display update cycle, to ensure that sensor data is as accurate as possible for the revised pose prediction. Pose prediction may extrapolate sensor data by integrating differential equations of motion. It may incorporate biomechanical models of the user, which may be learned by prompting the user to perform specific movements. Pose prediction may take into account a user&#39;s tendency to look towards regions of interest. Multiple parallel pose predictions may be made to reflect uncertainty in the user&#39;s movement.

This application is a continuation in part of U.S. Utility patent application Ser. No. 14/872,488 filed Oct. 1, 2015, which is a continuation in part of U.S. Utility patent application Ser. No. 14/788,633 filed Jun. 30, 2015, the specifications of which are hereby incorporated herein by reference.

BACKGROUND OF THE INVENTION

Field of the Invention

One or more embodiments of the invention are related to the field of virtual reality systems. More particularly, but not by way of limitation, one or more embodiments of the invention enable a virtual reality display system that renders images based on a predicted pose, and then corrects these rendered images using a revised predicted pose.

Description of the Related Art

Virtual reality systems are known in the art. Such systems generate a virtual world for a user that responds to the user's movements. Examples include various types of virtual reality headsets and goggles worn by a user, as well as specialized rooms with multiple displays. Virtual reality systems typically include sensors that track a user's head, eyes, or other body parts, and that modify the virtual world according to the user's movements. The virtual world consists of a three-dimensional model, computer-generated or captured from real-world scenes. Images of the three-dimensional model are generated based on the user's position and orientation. Generation of these images requires rendering of the three-dimensional model onto one or more two-dimensional displays. Rendering techniques are known in the art and are often used for example in 3D graphics systems or computer-based games, as well as in virtual reality systems.

A major challenge for existing virtual reality systems is combining realistic images with low-latency rendering, so that user's virtual reality experience matches the rapid feedback to movement observed in real environments. Existing systems often have long latency to measure changes in the user's position and orientation, and to rerender the virtual world based on these changes. 3D rendering is a complex and processor intensive operation that can take potentially hundreds of milliseconds. The result is that users perceive noticeable lag between their movements and the rendering of updated virtual environments on their displays. Three technology trends are compounding this challenge: (1) The complexity of 3D models is growing as more 3D data is captured and generated. (2) Resolution of virtual reality displays is increasing, requiring more computational power to render images. (3) Users are relying increasingly on mobile devices with limited processor capacity. As a result of these trends, high latency in rendering virtual reality displays has become a major factor limiting adoption and applications of virtual reality technology. There are no known systems that provide sufficiently low-latency rendering and display to generate highly responsive virtual reality environments given these technology constraints.

One factor contributing to rendering latency is the resolution of a virtual reality display. In general, displays with large numbers of pixels require more rendering computation and thus may experience greater latency. Displays known in the art typically consist of rectangular arrays of pixels with uniform pixel density throughout the display. However, human vision has high resolution only in the center of the field view. Therefore, rendering images at high resolution on the sides of a display may be unnecessary, and may contribute to higher latency without improving the user's experience.

Perceived latency between user movements and display updates to reflect these movements may be mitigated in some situations by predicting the user's future position and orientation, and rendering to the prediction rather than to the current measurements. Rendering based on a predicted pose is described for example in U.S. Utility Pat. No. 9,063,330, “Perception based predictive tracking for head mount displays”, to LaValle et al. However, known systems such as the system described in '330 make a single prediction of a user's future pose for each display update cycle, and then render images based on this single prediction. Since image rendering may be time consuming, the predicted pose may deviate substantially from the user's actual pose by the time rendering is complete. There are no known systems that perform a corrective step after an initial prediction and rendering cycle to take into account these possible changes in pose during rendering.

For at least the limitations described above there is a need for a predictive virtual reality display system with post rendering correction.

BRIEF SUMMARY OF THE INVENTION

One or more embodiments described in the specification are related to a predictive virtual reality display system with post rendering correction. Embodiments of the system render images using predictions of the future predicted position or orientation of a user, and then correct these images using efficient approximations to rerender images quickly. This efficient and rapid rerendering reduces latency and improves the user's virtual reality experience.

One or more embodiments of the system include one or more displays viewable by a user. For example, these displays may be embedded in virtual reality goggles or glasses. One or more embodiments also include one or more sensors that measure aspects of the user's position, orientation, or both. Aspects of the user's orientation and position are referred to as the user's “pose” in this specification. Pose sensors may for example measure movements of the user's head, or of the user's eyes, or more generally of any body part or parts of the user. Embodiments of the system include a pose analyzer that receives sensor data and determines the user's pose from this data. The pose information is passed to a scene renderer, which generates the 3D virtual reality display viewed by the user. This display shows a portion of a 3D scene model that is visible to the user based on the user's current pose. The 3D scene model is the model of the virtual world that the user navigates through by changing pose.

The scene renderer generates one or more 2D projections from the 3D scene model. In one or more embodiments, these projections may be generated using well known 3D graphics techniques, for example using virtual cameras and perspective projection transformations onto the view planes of the virtual cameras. The 2D projections are then transmitted to the displays.

In addition, one or more embodiments of the system include an image warper. The image warper is the system component that provides for low-latency virtual reality display via efficient rerendering of scenes. The image warper may for example monitor the pose changes of the user and rerender displayed images based on these pose changes. The rerendering performed by the image warper may be a rerendering approximation, rather than a full perspective projection from the original 3D scene model. For example, some embodiments perform rerendering approximations by warping display images in relatively simple ways to partially reflect the changes in the user's pose. These rerendering approximations may offer lower latency display updates, although in some embodiments they may not be fully realistic compared to the full rendering process.

One or more embodiments of the system perform approximate rerendering by calculating a pixel translation vector, and then translating pixels of the display by this pixel translation vector. Effectively the image warper in these embodiments may shift pixels in a calculated direction and by a calculated amount to approximate the effect of the user's movements on the display. This approximation is not full 3D rendering, but it can be performed very quickly in some embodiments, greatly reducing latency between user's movements and display updates.

One or more embodiments of the system may use hardware acceleration to modify the pixels of a display to perform approximate rerendering. For example, display hardware or graphics processing unit hardware may support commands to directly shift pixels based on a pixel translation vector. Implementing pixel translations or other approximate rerendering transformations in hardware may further reduce latency in one or more embodiments.

In one or more embodiments, the rerendering approximations performed by the image warper may only be performed if the pose changes of a user are below a particular threshold value. For large changes in pose, the approximations used by the image warper may become inadequate, and it may be preferable to perform a full 3D rendering despite the high latency. For small changes in pose, the rerendering approximations may be sufficiently realistic.

In one or more embodiments, multiple pose changes for a user may be received while a full 3D rendering process is executed. By the time the 3D rendering process has completed, the initial user pose that was used for the rendering may be out of date, since newer pose data is by then available. One or more embodiments may perform a post-rendering correction on the rendered images, using the image warper to apply updates to the rendered images prior to displaying them. These post-rendering corrections may improve synchronization between the displayed images and the user's current pose.

One or more embodiments of the system may use pose prediction to calculate or estimate the pose of a user at a future time when the rendering and display processes are complete. Pose prediction may reduce the apparent latency between changes in user pose and corresponding display updates. One or more embodiments may use pose prediction for full rendering, for image warping, or for both. Embodiments may use any desired technique for pose prediction, including for example simple extrapolation of pose changes. With pose prediction, the predicted pose is provided to the rendering or approximate rerendering processes, rather than the measured pose. The rendering process calculates virtual camera poses from the predicted pose values, and renders a scene based on these virtual camera poses. The image warper calculates pose changes using the difference between the predicted future pose and the previously calculated virtual camera pose from full rendering of the scene.

One challenge faced by some embodiments is that the image warping process may leave holes in the display images with missing pixels. For example, if all pixels are shifted to the right, then the left edge of the display will have a hole without pixel data. Embodiments may employ various approaches to handle these holes. In one or more embodiments, the 3D renderer may render 2D projections that are larger than the display area. Pixels outside the display area may be cached in these embodiments in an off-screen cache, and retrieved when performing image warping to fill holes.

Another approach to filling holes employed by one or more embodiments is to estimate pixel values for the holes based on the pixel values of nearby pixels. For example, in one or more embodiments pixel values from the boundaries of regions may be propagated into the holes to fill them. Simple propagation of boundary pixels into holes may in some cases result in visual artifacts. In one or more embodiments, blur transformations may be applied to pixels in the holes or near the holes to reduce these artifacts.

One or more embodiments may employ various types of rerendering approximations for image warping. One technique used by some embodiments is to generate a simplified 3D model from the 2D projections received from the scene rendered, and to reproject these simplified 3D models onto the updated view planes that correspond to changes in the user's pose. For example, one or more embodiments may create a simplified 3D model by mapping a 2D projection from rendering onto another plane in the simplified 3D model, where the distance of this plane from the user reflects an average or typical depth of the objects in the complete 3D scene model. The depth of such an average plane may be fixed, or it may be supplied by the scene renderer with each 2D projection. One or more embodiments may use other simplified 3D models, such as spherical or cylindrical surfaces for example.

For small changes in pose, rerendering approximations based on reprojecting from a simplified 3D planar model may be approximately equivalent to using a pixel translation vector to shift pixels in display images in response to pose changes. For example, one or more embodiments may calculate a pixel translation vector for a rotation of a user around axis {circumflex over (ω)} by a small angle Δθ as ({circumflex over (ω)}_(y)Δθ,−{circumflex over (ω)}_(x)Δθ), which is then scaled to the reflect the pixel dimensions of the display. This formula reflects that small angular rotations of a user's view approximately result in pixels shifting in response to the rotations, with the amount of shift proportional to the angle of rotation. Changes in user pose may also involve translations (linear motions of the user). For translations, the amount of shifting of pixels is also a function of the distance of objects from a user: the closer the object to the user, the more pixels shift in response to user translations. In one or more embodiments, a rerendering approximation may be estimated by a pixel translation vector using an average depth estimate z* for the distance between the user and the objects in the 2D projection. These embodiments may calculate a pixel translation vector for a user translation by small vector Δr as (−Δr_(x)/z*,−Δr_(y)/z*), which is then scaled to reflect the pixel dimensions of the display. This formula reflects that objects that are further away shift less than objects that are closer. It also reflects that pixels shift in the direction opposite to the movement of the user. One or more embodiments may user pixel translation vectors for rerendering approximations that combine the above effects of user rotation and user translation, such as for example ({circumflex over (ω)}_(y)Δθ−Δr_(x)/z*,−{circumflex over (ω)}_(x)Δθ−Δr_(y)/z*).

In summary, one or more embodiments of the invention enable a low-latency virtual reality display by using techniques to efficiently and approximately rerender images based on changes in the user's pose. Such techniques include, but are not limited to, shifting pixels by a pixel translation vector that is calculated from the user's movements. One or more embodiments may provide additional features such as filling of holes generated by image warping, and applying corrections prior to displaying rendered images to synchronize them with the user's current pose.

A major factor driving rendering time and latency is the number of pixels in a virtual reality display. Generally displays with more pixels require more rendering computation. Rendering latency may be reduced by using a low resolution display, but this approach may compromise the user experience. One or more embodiments of the invention may instead reduce rendering computation by rendering at lower resolution in selected portions of a display. The display hardware may have a high resolution, but not all regions of the display may be rendered at this resolution. Human vision has high resolution in the center of the field of view, but lower resolution on the sides of the field of view. One or more embodiments therefore may render a virtual environment at high resolution in the center of a display, and at lower resolution on the sides of the display. This approach may reduce latency without significantly compromising the user experience since the human mind may perceive items in an area where the focus is at a different resolution than other areas. In one or more embodiments, the center of the screen for each eye may be higher resolution that the outer edges of the screen (or screens). In other embodiments, or if programmatically altered via a user input, the center of the area that the eye is pointed at may be displayed at higher resolution, for example in embodiments that employ an eye tracker.

One or more embodiments incorporate a variable resolution virtual reality display system. The system may have one or more displays. The pixels of a display may be partitioned into regions, and rendering resolution may differ across regions. Each region may be portioned into a grid of grid elements, where each grid element contains one or more pixels. The ratio of pixels per grid element may vary across regions. For example, a high resolution center display region may have a ratio of 1 pixel per grid element, while relatively low resolution side display regions to the left and right of the center region may have a ratio of 4 or more pixels per grid element. These ratios are illustrative; one or more embodiments may have grids with any desired ratio of pixels per grid element. One or more embodiments may divide a display into any number and arrangement of display regions, with any desired pixel counts in the grid elements of each region.

One or more embodiments incorporate a 3D model of a scene, such as for example a virtual environment, and render this model onto the display or displays. The scene renderer that performs the rendering may project the 3D model onto the grid of each display region, and calculate a grid element value for each grid element from this projection. It may then assign pixel values for each pixel in the grid element based on the grid element value. Grid element values and pixel values may comprise for example, without limitation, any combination of color, intensity, hue, saturation, value, luminance, chrominance, lighting, material, texture, normal vector direction, transparency, size, shape, or pattern. Assigning pixel values based on grid element values may in one or more embodiments be a direct copying of the grid element value to the pixel values of each pixel within the grid element. One or more embodiments may perform any desired transformations to map grid element values into pixel values.

One or more embodiments may further optimize rendering using multiple geometry models at different levels of detail for one or more objects. For example, a scene renderer may select a level of detail for an object based entirely or in part on the resolution of the display region in which the object is rendered. A low level of detail model may be used for a low resolution display region, with for example multiple pixels per grid element; a high level of detail model may be used for a high resolution display region, such as for example a center region with one pixel per grid element.

One or more embodiments may use any rendering techniques known in the art. For example, one or more embodiments may use ray casting to render objects from a 3D model to a grid associated with a display region. Ray casting for a grid may for example project rays through a grid element towards objects in a 3D model, instead of through individual pixels as is typical in the art. Because low resolution display regions may have relatively small numbers of grid elements compared to pixels, efficiency of ray casting may be improved relative to a per-pixel ray casting approach.

One or more embodiments may use rasterization rendering techniques. For example, one or more embodiments may project geometric primitives from a 3D model onto an image plane associated with a display region, and then rasterize these projected primitives onto the grid for the display region. Rasterization may generate grid element fragments, which are then blended to form a final rasterized image on the display. Because the number of grid elements may be smaller than the number of pixels, rasterization to the grid may be more efficient that typical pixel-based rasterization.

Grid element fragments may comprise any information associated with a grid element, such as for example color, intensity, hue, saturation, value, depth, texture, normal direction, lighting, or material. In one or more embodiments a grid element fragment may also include a list of pixels associated with the fragment. In some cases rasterization may generate grid element fragments associated with all pixels contained within the grid element. However, one or more embodiments may rasterize projected primitives to a sub-grid-element level.

In one or more embodiments a 3D model may designate selected objects as high resolution objects, which for example may be rendered at a high resolution even in a low resolution display region. Other objects may be designated as low resolution objects. Rasterization of a high resolution object may for example generate grid element fragments with a single pixel per fragment, instead of fragments that comprise all pixels within a grid element. Low resolution objects may be rasterized to grid element fragments that contain all pixels within the grid element. One or more embodiments may select the resolution for rasterization of an object (such as single pixel or a complete grid element, for example) based on multiple factors instead of or in addition to the resolution of the display region in which the object is rasterized.

One or more embodiments may perform an initial rendering of images using a predicted future pose. The system may include a pose predictor that calculates a predicted pose at a future point in time, based for example on sensor data. The scene renderer may obtain an initial predicted pose from this pose predictor for a point in time when the displays will next be updated, and it may generate rendered images by using this initial predicted pose to create 2D projections from a 3D model. The display update time may be selected for example as the point in time when half of the pixels in the displays are updated. In a subsequent phase, an image warper may perform corrections on the rendered images by obtaining a revised predicted pose from the pose predictor. Since this revised predicted pose may be obtained at a time that is closer to the display update time, it may be more accurate than the initial predicted pose. The image warper may calculate a change in pose between the initial predicted pose and the revised predicted pose, and may generate a rerendering approximation to modify the 2D projections based on the change in pose. The modified 2D projections may then be transmitted to one or more displays for viewing.

In one or more embodiments the image warper may also apply a lens distortion correction to modify rendered images to account for lens effects. For example, lenses in virtual reality headsets may have either imperfections or deliberately engineered features that bend light for specific effects. The image warper may modify images to account for these lens effects.

One or more embodiments may delay the imaging warping corrections of the rendered images so that they occur just in time for a display update, for example when display updates occur at a regular refresh rate. This delay may improve the revised pose prediction, since it may occur closer to the display update time.

One or more embodiments may perform pose prediction using a current pose based on sensor data, and using one or more derivatives of the pose that may also be calculated based on sensor data. The current pose and the derivatives may be extrapolated to a future point in time to predict a future pose. For example, without limitation, a first derivative of pose may include an angular velocity, which may for example be obtained from a rate gyroscope. Extrapolation of a current pose and the derivative or derivatives of pose may be performed for example using numerical integration of differential equations. Numerical integration may be performed for example using Euler's method or the Runge-Kutta method.

One or more embodiments may incorporate a model of the user's body, for example a biomechanical model, into the pose prediction process. For example, the model may include limits on the range of motion, or a model of forces applied by or applied to parts of the user's body. One or more embodiments may generate a body model by analyzing sensor data. One or more embodiments may further prompt the user to make certain movements, and then analyze sensor data captured during the resulting movements to create the body model.

One or more embodiments may predict a future pose based on a region of interest that attract draw the user's attention. A region of interest may be for example a location in a 3D model that has action or that emits a sound, or a menu or other overlay on a display or in the 3D model.

In one or more embodiments the pose predictor may predict multiple future poses, each corresponding for example to a different scenario for how the user will move over a time period. A scene renderer may obtain multiple initial predicted poses, and may render images based on each of these different predictions. The image warper may then obtain a single revised predicted pose, select the initial predicted pose that is closest to the revised predicted pose, and warp the images rendered for this closest initial pose. Predicting multiple future poses may be particularly attractive for example in systems that use a highly efficient rendering pipeline, or in systems with parallel processing hardware that allows multiple pose predictions and renderings to be performed in parallel.

In one or more embodiments a rendered image may be divided into tiles, and image warping may be performed separately on each tile. The warping of each tile may for example use a pose prediction that predicts the pose at the time when that specific tile will be updated on a display. This tiling approach may further reduce the latency between changes in pose and the updates to the display to reflect these changes.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features and advantages of the invention will be more apparent from the following more particular description thereof, presented in conjunction with the following drawings wherein:

FIG. 1 illustrates the key components of at least one embodiment of low-latency virtual reality display system, configured for illustration with displays, sensors, and some processing modules embedded in virtual reality goggles, and rendering performed by a wirelessly connected mobile device.

FIG. 2 shows a high-level architectural view of the embodiment shown in FIG. 1.

FIG. 3 shows a swimlane diagram for the major rendering activities of the embodiment shown in FIG. 2.

FIG. 4 illustrates an embodiment of the system that implements low-latency rerendering using a pixel translation.

FIG. 4A illustrates an embodiment of the system that uses hardware accelerated rerendering using offset registers for reading frame buffer memory.

FIG. 5 illustrates an embodiment of the system that executes low-latency rerendering if the changes in a user's pose are below a threshold value.

FIG. 6 shows a swimlane diagram for the major rendering activities of the embodiment shown in FIG. 5.

FIG. 7 illustrates an embodiment of the system that performs a post-rendering modification to rendered scenes using a low-latency correction for recent changes in the user's pose.

FIG. 8 shows a swimlane diagram for the major rendering activities of the embodiment shown in FIG. 7.

FIG. 8A shows a swimlane diagram for an embodiment of the system that use pose prediction to reduce apparent latency between pose changes and display updates.

FIG. 9 illustrates an embodiment of the system that renders a scene larger than the display into an offscreen buffer, in order to fill holes generated by low-latency rerendering transformations.

FIG. 10 illustrates an embodiment of the system that fills holes generated by low-latency rerendering transformations by extending pixels from the image boundary.

FIG. 11 illustrates an embodiment of the system that fills holes generated by low-latency rerendering transformations by blurring pixels near the image boundary.

FIG. 12 illustrates an embodiment of the system that generates a low-latency rerendering transformation by projecting the rendered image onto a plane, and then rerendering onto a modified image plane corresponding the user's modified pose.

FIG. 13 shows a 2D model of an approximate rerendering calculation that generates a pixel translation vector from small angular rotations of a user's orientation.

FIG. 14 shows a 2D model of an approximate rerendering calculation that generates a pixel translation vector from translations of a user's position.

FIG. 15 illustrates an embodiment of the system that partitions a display into a high resolution center region and two low resolution side regions; rendering onto the low resolution regions uses coarser grid elements that comprise multiple pixels.

FIG. 16 illustrates another embodiment of a display partitioned into regions with different rendering resolutions.

FIG. 17 illustrates the rendering process for an embodiment with variable resolution display regions.

FIG. 18 illustrates an embodiment of the system that renders objects to a grid, which may be coarser than the pixel grid of a display, and then maps grid element values into pixel values.

FIG. 19 illustrates an embodiment of the system that renders different objects at different levels of detail, in addition to rendering different display regions at different resolutions.

FIG. 20 illustrates an embodiment that uses ray casting to render objects to a grid of grid elements, where a grid element may comprise multiple pixels.

FIG. 21 illustrates an embodiment that uses rasterization to generate grid element fragments from projected geometric primitives associated with objects.

FIG. 22 illustrates an embodiment that incorporates a list of pixel addresses into a grid element fragment.

FIG. 23 illustrates an embodiment that identifies selected objects as high resolution objects, and that renders these high resolution objects to individual pixels even in low resolution display regions.

FIG. 24 shows an architectural block diagram of an embodiment that generates display images by first rendering images based on an initial predicted future pose, and then warping the rendered images based on a revised predicted future pose.

FIG. 25 shows a flowchart of two parallel loops executed by one or more embodiments: a sensor update loop, and a display update loop. The display update loop performs pose prediction, rendering, and image warping.

FIG. 26 shows a simplified swimlane diagram for selected steps of the flowchart of FIG. 25.

FIG. 27 illustrates a potential issue with the swimlane diagram of FIG. 26: rendering and warping may complete prior to the display update cycle, causing the predicted poses used for rendering and warping to be out of date by the time the display update occurs.

FIG. 28 illustrates a solution to the issue of FIG. 27 that is employed in one or more embodiments: a delay is added after rendering and prior to warping, so that warping occurs just in time for the display update.

FIG. 29 shows a method for pose prediction that may be used in one or more embodiments; this method numerically integrates angular velocity and angular acceleration to predict future orientation.

FIG. 30 illustrates an embodiment that incorporates a physical model of range of motion limits into the pose prediction process.

FIG. 31 extends the physical model of FIG. 30 to incorporate a biomechanical model of forces applied by the body that affect pose prediction.

FIG. 32 illustrates an embodiment that learns parameters of a physical model of head motion by prompting the user to perform specific movements.

FIG. 33 shows an embodiment that identifies a region of interest in a scene, and that takes this region of interest into account in predicting a future pose by estimating that the user will tend to look towards the region of interest.

FIG. 34 illustrates an embodiment that predicts multiple possible future poses, renders images for each, and selects one of the rendered images for warping based on which predicted pose was the most accurate.

FIG. 35 illustrates an embodiment that partitions a rendered image into tiles, and that warps each tile separately.

FIG. 36 shows an illustrative timing diagram for the rendering, pose prediction, and warping steps for the tiled image shown in FIG. 35.

DETAILED DESCRIPTION OF THE INVENTION

A predictive virtual reality display system with post rendering correction will now be described. In the following exemplary description numerous specific details are set forth in order to provide a more thorough understanding of embodiments of the invention. It will be apparent, however, to an artisan of ordinary skill that the present invention may be practiced without incorporating all aspects of the specific details described herein. In other instances, specific features, quantities, or measurements well known to those of ordinary skill in the art have not been described in detail so as not to obscure the invention. Readers should note that although examples of the invention are set forth herein, the claims, and the full scope of any equivalents, are what define the metes and bounds of the invention.

FIG. 1 shows a high-level schematic diagram of an embodiment of the invention that embeds elements of the system into virtual reality goggles. Other embodiments may embed elements of the system into any other devices wearable by or viewable by one or more users. For example, without limitation, one or more embodiments may embed elements of the system into goggles, glasses, sunglasses, monocles, helmets, visors, binoculars, contact lenses, or ocular implants. Some embodiments may not be worn by users, but may be placed on walls, in televisions, in mirrors, on ceilings or floors, inside flight simulators or other simulators, in windshields, in windows, or in or on any other location where a virtual reality experience is desired.

In FIG. 1, user 101 wears a head-mounted device 120 that incorporates several elements of the embodiment shown. Displays 110 and 111 are in front of the user's left and right eyes, respectively. These displays are shown offset from user 101 for exposition; in reality many embodiments may position displays of head-mounted devices directly in front of the user's eyes. While the embodiment shown has two displays—one for each eye—embodiments may use any number of displays, including for example only a single display, or two displays as shown in FIG. 1, or more than two displays. In FIG. 1, the images shown on displays 110 and 111 are different; this may be useful in one or more embodiment for example to provide a stereoscopic 3D display. One or more embodiments may use the same image for multiple displays.

Device 120 includes a sensor (or multiple sensors 121). Sensor 121 measures some aspect of the position or orientation of user 101, or of changes thereto. The position and orientation of an object in three-dimensional space is referred to in the art as the “pose” of that object. Hence sensor 121 is a type of pose sensor. One or more embodiments may measure any desired aspects of the pose of any body parts of user 101. For example, in some embodiments sensor 121 may measure the pose of the user's head. In some embodiments sensor 121 may measure the pose of one or more of the user's eyes. Combinations of pose measurements for different body parts may also be used in one or more embodiments. Examples of sensors that may be used in one or more embodiments include, without limitation, accelerometers, gyroscopes, GPS trackers, ultrasonic rangefinders, pressure sensors, video cameras, altimeters, radars, sonars, magnetometers, flow meters, Doppler shift meters, or tilt sensors. Embodiments of the system may use only a single sensor, or multiple sensors. Some embodiments may use one or more sensors that directly measure some aspect of the pose of a body part of the user; for example, a magnetometer may provide partial orientation information directly. Some embodiments may use one or more sensors that indirectly measure pose; for example, a gyroscope may measure angular velocity, which must be integrated to yield orientation. The schematic of FIG. 1 shows sensor 121 located near the back of the head of user 101; this location is arbitrary and may vary in different embodiments of the invention. For example, an embodiment that uses a video camera eye tracker to measure the orientation of a user's eye may be mounted near the user's eyes. One or more embodiments may use multiple sensors at different locations of a user's body. One or more embodiments may use sensors that are not mounted on the user's body at all, but that measure some aspect of the pose of a user or one or more of the user's body parts. For example, one or more embodiments may use video cameras located near the user, and may analyze images from these cameras to determine the user's pose.

In FIG. 1, device 120 also includes pose analyzer 122. This element receives sensor data from the sensor or sensors 121, and uses this data to calculate the pose of one or more body parts of user 101. The calculations made by pose analyzer 122 will in general depend on the type of sensor or sensors 121. For example, one or more embodiments may use inertial sensors for the sensors 121, in which case the pose analyzer 122 may execute an inertial tracking algorithm to estimate the position and orientation of the user. Such inertial tracking algorithms are well known in the art. Embodiments may use any methodology to translate the raw sensor data into pose information. One or more embodiments may use more than one pose analyzer; for example, an embodiment with eye tracking sensors may use a separate pose analyzer for each eye. While FIG. 1 illustrates an embodiment with pose analyzer 122 mounted on device 120 that is attached to the user, embodiments may use pose analyzers that are not attached to the user, or may use a combination of pose analyzers on a user-mounted device and pose analyzers remote from the user.

In general a virtual reality device generates virtual reality display images based on the user's pose. For example, as a user moves or turns, different images are displayed to simulate the real experience of viewing different parts of a scene. This functionality requires a 3D model of one or more scenes, and a rendering system that renders views of the scene based on the user's pose. In the embodiment shown in FIG. 1, the 3D scene model 141 and the scene renderer 142 are located in mobile device 140. This mobile device 140 communicates with the head-mounted device 120 over a wireless network 130. This separation of functionality between a head-mounted device and a remote device is only illustrative; embodiments may use any desired architecture to organize elements of the system into devices. For example, in one or more embodiments, all elements of the system may be incorporated into a device such as head-mounted device 120 that is worn by a user. In one or more embodiments, all of the elements of the system may be remote from the user: for example, the user's orientation may be detected by video cameras in a room, the pose analyzer and scene renderer may execute on computers in the room, and the rendered images may be displayed on monitors mounted on the walls of the room. In one or more embodiments, the system may be a distributed system with elements distributed over multiple nodes that communicate over a network; for example a 3D scene model may be hosted on a remote server, rendering may be done on a device that is local to the user but not attached to the user, and the sensors and displays may be on a user-mounted device. Embodiments may use any type of network communication between elements of the system, including wired or wireless networks, or combinations thereof. Any network media and network protocols may be used to communicate between elements of the system.

3D scene model 141 contains a 3D representation of the objects that may be displayed to the user; it is a model of the 3D “virtual world.” This scene model may be static, or it may change over time. Dynamic 3D scene models may also change in response to user actions or to changes in user pose. The 3D scene model may include computer-generated elements, real scene data captured by cameras or 3D scanners, or combinations of computer-generated and real data. Embodiments may use any desired type of 3D scene model, and any desired data representation for the scene model such as for example, without limitation, VRML, X3D, OBJ, COLLADA, Blender, 3DS, or any other proprietary or open format for 3D information.

Scene renderer 142 generates one or more rendered 2D images from scene model 141. In one or more embodiments of the system, the scene render generates one or more “virtual cameras” based on the pose data received from pose analyzer 122. These virtual cameras have a location and orientation in the 3D space defined by the 3D scene model. In the embodiment shown in FIG. 1, scene renderer 142 generates two virtual cameras 150 and 151, each of which corresponds to one of the two displays 110 and 111. Embodiments may use any number of virtual cameras and associate these virtual cameras in any desired manner with displays. Rendering generates a 2D projection for each of the virtual cameras. Techniques for rendering 2D projections from 3D scenes are well known in the art, and these techniques are implemented in many readily available software libraries and graphics processing units. Embodiments may use any of the well known techniques, software packages, or devices for 3D rendering to generate 2D projections. In the embodiment illustrated in FIG. 1, virtual camera 150 generates 2D projection 160, and virtual camera 151 generates 2D projection 161. 2D projections 160 and 161 are transmitted back to device 120 over network 130. These projections may be displayed directly on displays 110 and 111.

In the embodiment shown in FIG. 1, device 120 includes image warper 123. The image warper provides a low-latency “rerendering” of the projections 160 and 161 for certain types of changes in the user's pose. Specifically, the image warper receives data on the virtual camera poses 150 and 151 that were used to generate projections 160 and 161. It also receives updates to the user's pose from pose analyzer 122. By comparing the user's new pose to the virtual camera poses used to render the 2D projections, the image warper calculates a change in pose. When a user's pose changes, the full rendering path to generate new 2D projections would require another iteration of the original rendering path: pose data would be sent to device 140, and converted to virtual camera poses 150 and 151; then scene renderer 142 would generate new 2D projections from 3D scene model 141, and transmit these new 2D projections back to device 120. This full rendering path may be relatively slow, leading to observable latency for the user. The function of the image warper is to reduce this latency by performing a rapid “rerendering approximation” that provides a relatively quick and efficient update to the images 110 and 111 based on changes to the pose. This rerendering approximation is not a complete rendering as would be performed by the scene renderer 142; instead it uses approximations to reduce the calculations and communications required to update the display, thereby reducing latency. Illustrative details of how various embodiments may perform image warping are provided below.

FIG. 2 shows a conceptual block diagram of the embodiment of FIG. 1, illustrating the main data paths. Sensor (or sensors) 121 generate sensor data 221. This sensor data may include, for example, angular velocity data, acceleration data, velocity data, or any other data generated by any of the types of sensors discussed above or any sensor that may measure any aspect of the pose of a user's body part. The sensor data 221 is sent to pose analyzer 122, which generates body pose 222 from the sensor data. Body pose 222 may include multiple poses, depending on the embodiment; for example in one or more embodiments with eye trackers, body pose 222 may have a separate pose for each of the user's eyes. Body pose 222 is sent to scene renderer 142, which takes 3D scene model 141, and renders one or more 2D projections such as 161. 2D projections 161 are sent to displays 111. The scene renderer 142 also generates virtual camera poses 242 for the virtual camera or virtual cameras used to generate the 2D projections. For some subsequent changes in pose, the new body pose 222 and the virtual camera pose 242 may be sent to image warper 123. Embodiments may use various techniques to determine when, whether, and how to use rerendering via the image warper vs. full rendering iterations via the scene renderer. Image warper 123 calculates a change in pose 250. The change in pose 250 and the original 2D projections 161 are sent to the rerendering approximation 260, which performs the image warper to transform 2D projection 161 into modified 2D projection 261, which is then sent to display 111. In some embodiments the rerendering approximation process may be repeated multiple times before another full rendering of the scene. Embodiments may employ various techniques for repeated rerendering approximations. In some embodiments for example the repeated rerendering may be “iterative”: warped projection 261 may be sent back to the rendering approximation 260 on path 271, for another iteration of warping when a new body pose 222 is available. In these iterative embodiments of repeated rerendering, the pose of the last warped image may also be provided on path 272 to the pose change calculation 250 so that pose changes represent only the change from the last warped image. In other embodiments the repeated rerendering may instead by “cumulative”: original 2D projection 111 may be saved, and repeated rerendering approximations may be performed on the original projection rather than on the last warped image. Some embodiments may employ combinations of these iterative and cumulative rerendering approaches.

FIG. 3 shows an illustrative “swimlane” process timing diagram of some of the key steps described above. This diagram presumes that a 3D scene has been previously rendered and is currently displayed on the displays. Initially the Pose Analyzer calculates a pose at 303, and sends this pose to the Scene Renderer. The Scene Renderer launches a Render process 301 which is time-consuming. If the system waited for the Render process 301 to complete, the display would not be updated until the new display 302 is available. To provide a lower latency display that is responsive to user's movements, the Pose Analyzer sends the pose 303 to the Image Warper as well. The Image Warper executes a rapid Rerender process at 304 to modify the current display based on the change in pose. This Rerender process finishes quickly resulting in new display 305. This example illustrates how the Image Warper provides for a lower latency virtual reality display, by executing a fast, approximate rerendering to update a display rather than waiting for a time-consuming full rendering process.

In FIG. 3, this process of rerendering repeats a second time while the Render process 301 is calculating, and then a third time when pose calculation 306 is sent to the Image Warper for rerendering, to generate display 308. After Render 301 is complete, the new 2D projection is available for subsequent rerendering steps. In this illustrative embodiment, full Rendering 301 and approximate Rerendering 304 are interleaved. Some embodiments may employ different strategies to mix full rendering and approximate rerendering as desired. The timing shown in FIG. 3 of three approximate rerendering steps occurring while full rendering is executing is simply illustrative; embodiments may employ any desired or required frequency and timing of these steps based on latency requirements, processor capacity, and the types of rerendering approximations used.

Embodiments of the system may employ various types of approximate rerendering techniques to achieve the desired latency. In one or more embodiments, the approximate rerendering consists of or includes a pixel translation that simply shifts all pixels of the 2D projection by an appropriate pixel translation vector. One advantage of this approach is that pixel translation can be executed very rapidly; for example in some embodiments it may be achieved simply by modifying an offset address for the display memory used by a graphics processing unit. In some embodiments pixel translation may be supported directly by the display hardware. FIG. 4 illustrates an embodiment that uses a pixel translation vector for rerendering approximation. Initially user 101 has a pose indicated by view vector 401 a. The user is observing 3D scene model 141 a, which includes for illustration three objects: a sphere 441 a, a pyramid 441 b, and a box 441 c. (These objects are illustrated in two dimensions in FIG. 4 for simplicity, but in general the 3D scene models may contain three dimensional shapes.) The objects are located at different distances from the user 101, with 441 a closest and 441 c furthest away. The render process 142 a generates 2D projection 161. As illustrated in 161, the rendering process shows the depth of the various objects, with the sphere 441 appearing largest since it is closest to the user. The rendering process also reflects occlusion of objects; since sphere 441 a is in front, it partially obscures objects 441 b and 441 c.

After this initial rendering, user 101 moves to the right, with new view vector 401 b. The new pose of the user (which reflects the new view vector) is compared to the original pose with the pose change comparator 250. This pose change is sent to the approximate rerender 260, which calculates a pixel translation vector 460 that approximates the change to the 2D projection based on the user's movement. Since the user moved to the right, the pixel translation vector is a shift of pixels leftwards. Applying the pixel translation vector 460 to the original 2D projection 161 results in modified image 261. All pixels in the scene are shifted left by the same amount.

FIG. 4 also illustrates how the rerendering approximation differs from a full rendering based on the new pose. If the new pose 401 b is sent to the Scene Rendering process 142 b, the resulting 2D projection is 462. This new 2D projection is a fully accurate representation of the user's new view. For example, in the updated 2D projection 462, the sphere 441 shifts leftward more than the box 441 c, since it is closer to the user. Because the rendering process 142 b takes the depth of the objects into account in rendering the scene, these relative shifts are correctly rendered. In contrast, the approximate rerendering 260 via pixel translation vector 460 captures the basic movement of the scene—the user moves right so the pixels shift left—but it is nevertheless an approximation that does not take into account the 3D scene model. The advantage of the approximate rerendering is that it can be performed very quickly, particularly with pixel translations, resulting in low latency display that is very responsive to the user's movements. Different embodiments of the system may mix full rendering and approximate rerendering as needed or desired to make the appropriate tradeoffs between accuracy and low latency based on the application.

One or more embodiments of the system may use hardware acceleration to modify the pixels of a display to perform pixel translations or other image warping operations. FIG. 4A illustrates an example of an embodiment with hardware support for pixel translation in the monitor hardware. In some embodiments hardware support may be provided in graphics processing units or in other system components as well. In FIG. 4A, monitor 110 includes hardware 4A01 that drives the monitor output. This hardware has direct support for implementing pixel translation 460. The monitor hardware includes a frame buffer 4A02 that stores pixel values. To display the pixel value at a screen address 4A05, corresponding for example to pixel 4A04 on the display 110, the hardware adds offsets 4A03 to the screen address 4A05 to obtain a frame buffer address 4A06, which in this example points to frame buffer pixel 4A07. The offset 4A03 is set based on pixel translation 460. Changes to the pixel translation can be rerendered very quickly by the display hardware by updating the offset 4A03. In one or more embodiments the display hardware may provide support for additional image warping features, such as for example filling of holes with interpolated pixel values, blurring of edge regions, rotations in addition to translations, or any other desired warping transformations. One or more embodiments may provide hardware acceleration in other system components instead of or in addition to in display hardware, such as for example in graphics processing units or in coprocessors.

In one or more embodiments, approximate rerendering may be used only when a user makes relatively small changes in pose. In some cases the accuracy of approximate rerendering may be very good for small changes in pose, but it may be poorer for large changes in pose. Therefore limiting approximate rerendering to small changes in pose may be appropriate in some embodiments. FIG. 5 illustrates an embodiment that employs this strategy. The virtual camera pose 242 used to generate a previous 2D projection is compared to a user's current pose 222 to generate a change in pose 250. This change in pose is compared at 501 to a threshold. If the change in pose is below a threshold, rerendering approximation 260 is executed for a low latency update to the display; otherwise a full rendering 142 is executed to generate new 2D projections 161. Embodiments may use various methods to compare pose changes to threshold values. For example, for pose changes that are translations, the distance moved by the user may be a metric that is compared to a threshold value. For pose changes that are rotations, the angle of rotation may be a metric that is compared to a threshold value. For pose changes that combine translations and rotations, weighted sums of translation distance and angular change may be compared to a threshold, or translations and angle changes may each be employed to respective thresholds. These examples are illustrative; embodiments may use any desired function to compare pose changes to any threshold value or values to decide when to execute approximate rerendering.

FIG. 6 shows an illustrative swimlane timing diagram for the embodiment shown in FIG. 5 that compares pose changes to a threshold. Pose change 601 is determined to be a small change since it is below the threshold value. Therefore the rerendering approximation 304 is executed to generate display 304. Similarly the next 2 pose changes are small, and rerendering approximations are executed. Afterwards pose change 602 is determined to be large (greater than the threshold); therefore a full rendering operation 301 is initiated. In this illustrative embodiment, the system pauses display updates during time 610 while the rendering process 301 is executing. Thus the next update to the display 302 occurs when rendering 301 is complete.

In some embodiments, naïve parallel interleaving of full rendering and approximate rerendering may result in display updates that appear to be out of sequence. Returning to FIG. 3, the three approximate rerendering steps beginning at 304 execute in parallel with the full rendering process 301. While this parallelism achieves low-latency update of displays (for example at 306 and 308), it may result in timing artifacts that negatively affect the user's experience. For example, the user observes display update 308, which is based on the user's pose 306. Immediately afterwards, the user observes display update 302, which is based on the user's pose 303. Thus the display at 302 may appear to the user to go backwards relative to the most recent display 308 which was generated by a rerendering approximation. For very small changes in pose these artifacts may not be noticeable, but in some embodiments they may compromise the virtual reality experience.

One solution to these timing artifacts is to prevent parallel execution of full rendering and approximate rerendering altogether. Such an embodiment is illustrated in FIG. 6. In this embodiment, approximate rerendering occurs for small pose changes, and full rendering occurs for large pose changes. Moreover, approximate rerendering is paused during full rendering. Thus the user never observes the timing issues that may be visible for example in FIG. 3. However, the embodiment illustrated in FIG. 6 achieves this consistency at the expense of latency: for example the delay 610 in display updates during rendering 301 may be perceived by the user as a lack of responsiveness of the system.

Embodiments of the system may employ a more sophisticated interleaving strategy that achieves consistently low latency without introducing the types of timing artifacts illustrated in FIG. 3. These embodiments generate full rendering in parallel with approximate rerendering, and in addition they perform post-rendering corrections on the fully rendered images to synchronize them with updates that have occurred since the full rendering process began. FIG. 7 illustrates an embodiment that applies post-rendering corrections, and FIG. 8 shows an associated swimlane diagram for the key processing steps. Turning first to FIG. 8, in this illustrative embodiment, small changes in pose generate approximate rerendering, and large changes in pose generate full rendering. For example, pose change 601 is small (compared to a designated threshold value); hence approximate rerendering 304 is executed to generate display update 305, with relatively low latency. Similarly the subsequent two pose changes are small and generate approximate rerendering. Pose change 602 is large; hence the system initiates full rendering 301 which is based on the pose at 602. Because rendering 301 is time-consuming, pose changes 801, 802, and 803 are received during rendering 301. Since each of 801, 802, and 803 are small changes, rerendering approximations are performed to generate display updates for each of these pose changes. After rendering 301 completes, instead of displaying the output of 301 directly, the output of 301 is corrected by process 801 before it is displayed. The correction 810 uses the cumulative pose changes 801, 802, and 803 that occurred after the initiation of 301 to synchronize the display with the most recent pose.

FIG. 7 shows a block diagram of an embodiment that implements the process illustrated in FIG. 8. At time t₁ pose 222 a is sent to renderer 142. Eventually the renderer generates 2D projection 161 a; this projection was based on virtual camera pose 242 a, which corresponds to pose 222 a at time t₁. One or more pose updates have been received and processed between time t₁ and the availability of 2D projection 161 a; the most recent such update is body pose 222 b received at time t₂. Therefore the 2D projection 161 a is not sent directly to display 111. Instead it is sent to image warper 123, which will correct it for pose changes that have occurred since the beginning of the rendering process. Image warper 123 calculates virtual camera pose 242 b corresponding to the most recent body pose 222 b, and compares it to the virtual camera pose 242 a used for rendering projection 161 a. The difference in these virtual camera poses is applied to post rendering correction 701, which modifies 2D projection 161 a based on recent pose changes to generate corrected 2D projection 161 b, which is sent to display 111. One potential benefit of such an embodiment is that displayed images will reflect the most recent pose data received from the sensors. Another potential benefit is that approximate rerendering may be interleaved in parallel with full rendering for improved latency without introducing timing artifacts.

Approximate rerendering and post rendering correction may significantly reduce the latency between changes in pose and updates to the display that reflect these changes. However, the processes of measuring pose, generating an approximate rerendering, and transferring data to the display, continue to introduce some latency even when these improvements are in place. FIG. 8A illustrates this potential issue. A pose measurement starts at time 8A01 (t₁). After pose measurement completes, a rerendering approximation is calculated and transferred to the display; the display update competes at time 8A02 (t₂). Although a long-latency full rendering is avoided, there remains elapsed time 8A03 (Δt) between the start of pose measurement and the completing of the display update. The display update therefore lags the true pose by this amount Δt.

One or more embodiments may employ pose prediction to further reduce this latency. An example of this approach is illustrated in the lower half of FIG. 8A. A pose measurement 8A05 occurs with resulting pose Q₁. Instead of passing this pose Q₁ directly to the image warper, the system uses the known delay 8A03 (Δt) between pose measurement and display to predict what the pose will be at the time 8A30 that the display update will complete. In this illustrative embodiment, an extrapolation of pose changes is made using the previous pose sample 8A04, which measured pose Q₀. Assuming sampling interval Δs between pose measurements, a pose predication 8A06 is calculated as Q₂=(Q₁Q₀ ⁻¹)^((Δt/Δs))Q₁. This calculation considers poses to be rigid body transformations of three-dimensional space, with multiplication used to represent composition of these transformations. The predicted pose 8A20 (Q₂) is provided to the image warper for the rerendering approximation. Thus the display process which completes at time 8A30 is synchronized with the time of the predicted pose used to generate the display.

This pose prediction calculation 8A06 is an illustrative example; one or more embodiments may use any method to predict a future pose based on one or more previous pose samples and on any other available information. Any method of predicting a future trajectory for the location or orientation of any body part may be used by one or more embodiments. Prediction methods used by one or more embodiments may also for example take into account known constraints on the motion of the user. One or more embodiments may use adaptive pose prediction techniques that monitor the user's movements over time to predict the most likely subsequent movements based on previous movements.

FIG. 8A illustrates the use of pose prediction for image warping. One or more embodiments may use similar pose prediction techniques for full rendering as well. The discussion above for pose prediction for image warping applies to full rendering as well. One or more embodiments may generate a predicted pose that is sent to the full rendering process, where the predicted pose takes into account expected pose changes between the time of the pose measurement and the completion of the display update after full rendering. One or more embodiments may use pose prediction techniques for either or both of image warping and full rendering.

In some embodiments the approximate rerendering transformations applied by the image warper may result in “holes” in the transformed images with missing pixels. For example, returning to the embodiment illustrated in FIG. 4, the image warper shifts pixels to the left via pixel translation vector 460. This results in a “hole” 470 on the right edge of transformed image 261 that is missing pixels. Embodiments may employ various strategies or combinations of strategies to handle these holes. A very simple strategy employed by one or more embodiments is to fill holes with a relatively “neutral” background color; in some applications this may provide sufficient realism for small pose changes. However in other applications this simple approach may not be sufficient.

One or more embodiments may fill holes by rendering 2D projections that are larger than the displays. In these embodiments warping of the larger 2D projection may result in an updated projection that still fits entirely within the display area. FIG. 9 illustrates an embodiment that employs this strategy. In this embodiment, the scene renderer generates an extended 2D projection 901 from 3D model 141; this extended projection is larger than the display area. The displayed image 161 is a subset of the rendered area 901. For illustration we show the effect of an image warper 123 that applies a rightward pixel translation to the image. An embodiment that did not employ a hole-filling strategy would generate transformed image 111 a, which has missing pixels in region 911 on the left edge of the display. In the embodiment of FIG. 9, the pixels of the extended rendered projection 901 are saved in an offscreen cache. The image warper then pulls pixels from this offscreen cache as needed to fill holes generated by the warping. In FIG. 9, pixels from the mountain object 920 are pulled from the offscreen cache to fill hole 911, resulting in an improved rerendered projection with object 921 filling the hole. Embodiments may use any desired size and shape for the offscreen pixel cache.

One potential drawback of the strategy of generated an extended rendered area is that it requires additional processing for the rendering of more pixels; thus it may exacerbate latency issues due to rendering delays. One or more embodiments may employ a hole-filling strategy that instead generates pixel values for the missing pixels based on some features of the warped image. For example, the embodiment of the system illustrated in FIG. 10 fills in pixel values by propagating pixels outward from the boundaries of the warped image into the regions with holes. For illustration, image warper 123 shifts pixels of 2D projection 161 to the right, resulting in hole 911 that is missing pixels. In this embodiment, the image warper finds the boundary 1001 that corresponds to the original left edge of projection 161; it then propagates pixel values from this boundary to the left with propagation 1002. This pixel propagation results in filled region 1010 rather than the hole 911. In this illustrative embodiment, the resulting image 111 c has no noticeable hole; however the resulting shape of the mountainous area does not correspond precisely to the shape in the original 3D scene model 141. Nevertheless this simple strategy of propagating pixels from the boundary may provide adequate realism in some applications. One or more embodiments may employ other strategies to approximate pixel values in holes; for example one or more embodiments may locate a series of pixels in the warped image that are relatively close to the location of a missing pixel, and interpolate these pixel values to fill the hole.

Because pixel-filling approaches that propagate pixels from boundaries (or use similar heuristics) result in regions on the edges of displays that are not entirely faithful to the original 3D scene model, one or more embodiments may employ various blurring approaches to make these regions appear less sharp. By blurring the filled in regions, the approximate pixel values may be less noticeable to the viewer. FIG. 11 illustrates an embodiment that utilizes such a blurring. As before, the image warper shifts pixels to the right, resulting in hole 911 in warped image 111 a. Then blurring transformation 1110 is applied to the pixels in hole 911. The illustrative blurring transform 1110 simply averages pixel values across a square region center centered at the coordinates of each missing pixel. The resulting blurred region 1111 in 111 c has no obvious hole with missing pixel values; moreover the blurring has no obvious artifacts like the flat mountaintop showing in FIG. 10, region 1010. The blurring transformation 1110 which averages values in a local neighborhood is simply illustrative; embodiments may employ any desired transformation on the pixels of regions with holes, or on any pixels near to these regions, to achieve a desired blurring effect. For example, instead of a simple averaging, a Gaussian blur filter may be employed by one or more embodiments.

We now discuss illustrative approaches for image warping transformations. These transformations are rerendering approximations, rather than full rendering from the 3D scene model. In one or more embodiments, a rerendering approximation is generated by first creating a simplified 3D model from the 2D projections, and then reprojecting this simplified 3D model onto new view planes based on user's modified pose. For example, a simplified 3D model may be formed by mapping the 2D projections generated by the renderer onto one or more surfaces in 3D space. FIG. 12 illustrates an embodiment of the system that uses this approach for approximate rerendering. 3D scene model 141 a consists of three objects: a sphere 441 a close to user 101, a pyramid 441 b further from the user, and a box 441 c furthest from the user. FIG. 12 shows a two-dimension projection of the 3D scene model onto the y-z plane; here the z-axis points towards the user and the user is located at z=0 (a convention often used in 3D graphics applications), the y-axis points upwards, and the x-axis points towards the user's right. The sphere is at distance z_(s) from the user; the pyramid is at distance z_(p) from the user; and the box is at distance z_(b) from the user. (These z-values are negative, in conformance with the orientation of the z-axis.) Scene renderer 142 a generates 2D projection 161 of the 3D model. User 101 then changes pose, and image warper 123 performs a rerendering approximation to generate modified image 261. The rendering approximation first projects the 2D projection 161 onto plane 1211 in simplified 3D model 1210; this plane 1211 is at distance z* from the user. The value z* may be fixed, or it may be provided by the scene renderer 142 a based on an average or typical distance of objects in the 3D model 141 a from the user. In the simplified 3D model 1210 used by the image warper, all objects appear in 3D space at the same depth z* from the user, because all objects have been projected onto the single plane 1211 with depths 1212 of z_(s)=z_(p)=z_(b)=z*. This does not match the actual depths 1201 a, 1201 b, and 1201 c in the original 3D scene model 141 a; hence the image warper is employing an approximate rerendering for efficiency, which simplifies the 3D rerendering model 1210 compared to the real 3D scene model 141 a.

From the plane 1211 at depth z*, the image warper reprojects pixels onto modified view plane 1220 corresponding to the user's new pose. The orientation of plane 1220 is based on data received from pose analyzer 122. This reprojection generates modified image 261. In the illustrative example shown in FIG. 12, view plane 1220 is rotated clockwise compared to the initial view plane for image 161; hence the objects in 261 are rotated counterclockwise to form the rerendering approximation.

The embodiment illustrated in FIG. 12 generates a rerendering approximation by mapping the original 2D projection onto a single plane parallel to the user's original view plane, and then reprojecting that plane onto the user's modified view plane. One or more embodiments may map 2D projections onto other surfaces to perform approximate rerendering. For example, some embodiments may multiple portions of the 2D projections onto multiple planes. One or more embodiments may map 2D projections onto one or more curved surfaces, such as for example a sphere or a cylinder.

Mathematically, one or more embodiments may implement the rerendering approximation illustrated in FIG. 12 as follows. This implementation is illustrative only; embodiments may employ any desired transformations, algorithms, mappings, or image warpings to perform rerendering approximations. We assume for ease of illustration that a 2D projection is a rectangular image w pixels wide and h pixels high, and that the width w represents a horizontal field of view of f radians. We assume that the 2D projection was generated using a perspective projection transform of the 3D scene model onto view plane z=−1, followed by a scaling from spatial coordinates to pixel coordinates of

$s = {{w/2}\; \tan {\frac{f}{2}.}}$

The view plane z=−1 is mapped onto plane z=−z* to form the 3D model for rerendering; thus point (x,y) of the view plane is mapped to coordinates (z*x, z*y,−z*). The subsequent change to the user's pose is modeled as a rigid body transformation T of the view plane, which in general consists of a rotation R of angle Δθ around unit vector axis {circumflex over (ω)} followed by a translation by vector Δr. Each point (z*x,z*y,−z*) is then projected onto this new view plane, and rescaled from spatial coordinates to pixel coordinates by the same scaling factor of

${s = {{w/2}\; \tan \frac{f}{2}}},$

to generate the rerendering approximation.

Derivation of the projection onto the new view plane may be simplified by recognizing that transforming the view plane by transformation T is equivalent to transforming the points on the plane z=−z* by T⁻¹, and then mapping these points to the original view plane z=−1. Mapping points to the view plane z=−1 is straightforward: point (x,y,z) maps to

$\left( {{- \frac{x}{z}},{- \frac{y}{z}},{- 1}} \right).$

Thus the rerendering approximation includes the following steps:

$\left. \left( {x,y} \right)\rightarrow\left( {{z^{*}x},{z^{*}y},{- z^{*}}} \right) \right. = {\left. \left( {x_{0},y_{0},z_{0}} \right)\rightarrow{T^{- 1}\left( {x_{0},y_{0},z_{0}} \right)} \right. = {\left. \left( {x_{1},y_{1},z_{1}} \right)\rightarrow\left( {{- \frac{x_{1}}{z_{1}}},{- \frac{y_{1}}{z_{1}}}} \right) \right. = \left( {x^{\prime},y^{\prime}} \right)}}$

Mapping T⁻¹ consists of a translation by vector −Δr followed by a rotation R of angle −Δθ around unit vector axis {circumflex over (ω)}. We now consider the case of small changes in the user's pose, where both Δr and Δθ are small. In this case, rotation R can be approximated as R≈I+S({circumflex over (ω)})Δθ, where S is the cross-product matrix (S(u)v=u×v), and I is the identity matrix. For small changes, the effects of translation and rotation are approximately additive; thus T⁻¹r≈r−Δr−({circumflex over (ω)}×r)Δθ. Letting Δr=(Δr_(x),Δr_(y),Δr_(z)) and {circumflex over (ω)}=(ω_(x),ω_(y),ω_(z)) we have T⁻¹(x₀,y₀,z₀)=(x₀−Δr_(x)−ω_(y)z₀Δθ+ω_(z)y₀Δθ,y₀−Δr_(y)+ω_(x)z₀Δθ−ω_(z)x₀Δθ,z₀−Δr_(z)−ω_(x)y₀Δθ+ω_(y)x₀Δθ). Thus

$\begin{matrix} {x^{\prime} = {- \frac{x_{0} - {\Delta \; r_{x}} - {\omega_{y}z_{0}{\Delta\theta}} + {\omega_{z}y_{0}{\Delta\theta}}}{z_{0} - {\Delta \; r_{z}} - {\omega_{x}y_{0}{\Delta\theta}} + {\omega_{y}x_{0}{\Delta\theta}}}}} \\ {= {- \frac{{z^{*}x} - {\Delta \; r_{x}} + {\omega_{y}z^{*}{\Delta\theta}} + {\omega_{z}z^{*}y\; {\Delta\theta}}}{{- z^{*}} - {\Delta \; r_{z}} - {\omega_{x}z^{*}y\; {\Delta\theta}} + {\omega_{y}z^{*}x\; {\Delta\theta}}}}} \\ {= \frac{x - \frac{\Delta \; r_{x}}{z^{*}} + {\omega_{y}{\Delta\theta}} + {\omega_{z}y\; {\Delta\theta}}}{1 + \frac{\Delta \; r_{z}}{z^{*}} + {\omega_{x}y\; {\Delta\theta}} - {\omega_{y}x\; {\Delta\theta}}}} \end{matrix}$ and $\begin{matrix} {y^{\prime} = {- \frac{y_{0} - {\Delta \; r_{y}} + {\omega_{x}z_{0}{\Delta\theta}} - {\omega_{z}x_{0}{\Delta\theta}}}{z_{0} - {\Delta \; r_{z}} - {\omega_{x}y_{0}{\Delta\theta}} + {\omega_{y}x_{0}{\Delta\theta}}}}} \\ {= {- \frac{{z^{*}y} - {\Delta \; r_{y}} - {\omega_{x}z^{*}{\Delta\theta}} - {\omega_{z}z^{*}x\; {\Delta\theta}}}{{- z^{*}} - {\Delta \; r_{z}} - {\omega_{x}z^{*}y\; {\Delta\theta}} + {\omega_{y}z^{*}x\; {\Delta\theta}}}}} \\ {= \frac{y - \frac{\Delta \; r_{y}}{z^{*}} - {\omega_{x}{\Delta\theta}} - {\omega_{z}x\; {\Delta\theta}}}{1 + \frac{\Delta \; r_{z}}{z^{*}} + {\omega_{x}y\; {\Delta\theta}} - {\omega_{y}x\; {\Delta\theta}}}} \end{matrix}$

These expressions can be further simplified for the case of small x and y, which corresponds to pixels relatively near the center of the original 2D projection. Continuing to assume that both Δr and Δθ are small, many of the terms above are second-order expressions, such as for example yΔθ. Ignoring these second order terms, we have approximately:

$x^{\prime} \approx \frac{x - \frac{\Delta \; r_{x}}{z^{*}} + {\omega_{y}{\Delta\theta}}}{1 + \frac{\Delta \; r_{z}}{z^{*}}}$ $y^{\prime} \approx \frac{y - \frac{\Delta \; r_{y}}{z^{*}} - {\omega_{x}{\Delta\theta}}}{1 + \frac{\Delta \; r_{z}}{z^{*}}}$

Furthermore for small Δr the denominator can be ignored to first order, since

${\frac{1}{1 + {\Delta \; {r_{z}/z^{*}}}} \approx {1 - {\Delta \; {r_{z}/z^{*}}}}},$

and the product of Δr_(z)/z* with the terms in the numerators consists of second order terms. Thus we can use the rerendering approximation:

$x^{\prime} \approx {x - \frac{\Delta \; r_{x}}{z^{*}} + {\omega_{y}{\Delta\theta}}}$ $y^{\prime} \approx {y - \frac{\Delta \; r_{y}}{z^{*}} - {\omega_{x}{\Delta\theta}}}$

Using this approximation, all coordinates (x,y) are therefore shifted uniformly by translation

$\left( {{\Delta \; x},{\Delta \; y}} \right) = {\left( {{{- \frac{\Delta \; r_{x}}{z^{*}}} + {\omega_{y}{\Delta\theta}}},{{- \frac{\Delta \; r_{y}}{z^{*}}} - {\omega_{x}{\Delta\theta}}}} \right).}$

This formula provides the coordinate translation in spatial coordinates of the simplified 3D model. To convert to pixel coordinates, we simply apply the scaling factor

$s = {{w/2}\; \tan {\frac{f}{2}.}}$

This yields the pixel translation vector (sΔx, sΔy).

This derivation shows that an approximate rerendering can be performed using a simple pixel translation vector which is based on a simplified 3D model, and which is a good approximation for small pose changes and for pixels near the center of a display. The derivation shown considers both rotational pose changes and translational pose changes. One or more embodiments may consider only rotational pose changes. These embodiments may for example use a pixel translation vector of (sΔx,sΔy)=(sω_(y)Δθ,−sω_(x)Δθ), which uses only the rotational components of the pixel translation vector. One or more embodiments may consider only translational pose changes. These embodiments may for example use a pixel translation vector of

${\left( {{s\; \Delta \; x},{s\; \Delta \; y}} \right) = \left( {{- \frac{s\; \Delta \; r_{x}}{z^{*}}},{- \frac{s\; \Delta \; r_{y}}{z^{*}}}} \right)},$

which uses only the translational components of the pixel translation vector. One or more embodiments may consider both rotational pose changes and translational pose changes. These embodiments may for example use the complete pixel translation vector derived above of

$\left( {{s\; \Delta \; x},{s\; \Delta \; y}} \right) = {\left( {{{- \frac{s\; \Delta \; r_{x}}{z^{*}}} + {s\; \omega_{y}{\Delta\theta}}},{{{- s}\; \omega_{x}{\Delta\theta}} - \frac{s\; \Delta \; r_{y}}{z^{*}}}} \right).}$

The pixel translation vector approximation derived above is only one of many possible approximations to rerendering. One or more embodiments may use other approximations, or use the exact expressions derived above, to perform rerendering approximations.

Rerendering approximations using the above derived pixel translation vector are illustrated in FIGS. 13 and 14. FIG. 13 illustrates an example of a pose change consisting of a small angular rotation around the y axis. FIG. 13 shows a top view of the transformations, with the coordinate system 1301; the y axis points out of the page. Initially the user has pose 101 a, and the 2D projection generated from the 3D scene model has a circle at x-coordinate 1303 a (which is 0 since it is at the center of the display), and a square at x coordinate 1304 a, which is at angle 1306 (α). The rerendering approximation first maps these objects from the view plane 1302 a onto plane 1305, located at distance z* from the user. The user then changes pose to 101 b, by rotating the view vector clockwise around the y axis by angle Δθ. The objects on plane 1305 are then reprojected on the new view plane. The circle, which was originally at x₀=0, has new x coordinate 1303 b in the new view plane, with value x₀′=tan Δθ. Since we presume that Δθ is small, tan Δθ≈Δθ. The square which was originally at x₁ has new x coordinate 1304 b in the new view plane, with value x₁′=tan(Δθ+α). If both Δθ and α are small, then tan(Δθ+α)≈ tan Δθ+tan α≈Δθ+x₁. Thus both points x₀ and x₁ are shifted approximately by amount Δθ. This result corresponds to the pixel translation vector formula derived above, with ω_(y)=1, ω_(x)=Δr_(x)=Δr_(y)=0.

FIG. 14 illustrates an example of a pose change consisting of a small translation along the x-axis by amount Δr. The initial user pose 101 a, 2D projection 1302 a, and mapping to plane 1305 are identical to those of FIG. 13. The user then changes pose to 101 c, by moving to the right by amount 1401 (Δr). The view plane also moves to the right, the origin of the new x′ axis 1402 c perpendicular to the user's new position at point 1410. Objects on plane 1305 are then reprojected on the new view plane. The circle, which was originally at x₀=0, has new x coordinate 1403 c in the new view plane, with value x₀′=−Δr/z*. The square which was originally at x₁ has new x coordinate 1404 c in the new view plane, with value x₁′=x₁−Δr/z*. This result corresponds to the pixel translation vector formula derived above, with Δr_(x)=Δr, ω_(x)=ω_(y)=Δr_(y)=0.

One or more embodiments of the invention may use variable resolution rendering across one or more displays. The resolution of human vision is highest in the center of the field of view; therefore reduced resolution rendering on the sides of the field of view may not compromise a user's viewing experience. Moreover, reducing rendering resolution in selected display regions may improve rendering speed, and hence may reduce latency. Although shown in most examples as higher resolution in the center of the screen, embodiments may employ eye tracking to provide higher resolution at the area where an eye is pointed as well. In one or more embodiments, the center of the screen for each eye may be higher resolution that the outer edges of the screen (or screens). In other embodiments, or if programmatically altered via a user input, the center of the area that the eye is pointed at may be displayed at higher resolution, e.g., in embodiments that employ an eye tracker.

FIG. 15 illustrates an embodiment of the system that uses variable resolution rendering, for example to reduce rendering time and improve latency. Display 111 e is partitioned into three regions: center region 1501, left region 1502, and right region 1503. This partitioning is illustrative; one or more embodiments may partition any number of displays into any number of regions. Regions may be of any size, shape, and dimensions. In the example shown in FIG. 15, display 111 e comprises a rectangular array of pixels, such as pixel 1531. This is a common configuration, but embodiments may employ displays of any shape, size, dimensions, and pixel arrangements. The pixel densities (for example, per square centimeter) of regions 1501, 1502, and 1503 are approximately equal. Thus in this illustrative example, the hardware resolutions of the display regions are approximately equal. The system uses rendering optimizations to make the effective rendered resolution of the side regions 1502 and 1503 less than the effective rendered resolution of the center region 1501. The system partitions each display region into a grid of grid elements, where a grid element may comprise one or multiple pixels. For example, center region 1501 is partitioned into grid 1511, with one pixel per grid element. For example, grid element 1521 in grid 1511 contains only pixel 1531. Left region 1502 is partitioned into grid 1512, and right region 1503 is portioned into grid 1513. These side grids have 4 pixels per grid element. For example, grid element 1522 in left grid 1512 contains the 4 pixels 1532. Similarly grid element 1523 in right grid 1513 contains 4 pixels. Higher ratios of pixels per grid element correspond to lower resolution rendering in those regions. By combining pixels in the side regions into larger grid elements, the system may reduce the computational requirements for rendering in these regions, thus improving performance and potentially reducing latency.

As an illustration of the potential savings in rendering time and computation resources achieved by one or more embodiments of the invention, assume for example that a display contains p pixels, and that a fractions of the pixels of a display are contained in one or more low resolution regions, wherein the ratio of pixels per grid element is greater than 1. Assume also that the average ratio of pixels per grid element in these low resolution regions is r. Rendering time may be roughly proportional to the number of grid elements e, which is e=p(1−s)+ps/r. The rendering time for an un-optimized system (where each pixel is separately rendered) is proportional to p. Thus the ratio of rendering time for an embodiment of the system to the rendering time for an un-optimized system (which renders each pixel separately) is f=(1−s)+s/r. For the embodiment illustrated in FIG. 15, s=0.6, r=4, f=0.55. Thus the rendering time is reduced by almost a factor of 2, given these operational parameters in this embodiment.

The regions and grids shown in FIG. 15 are illustrative. One or more embodiments may use any partition of displays into regions, and may group pixels into grid elements of any shape and size. FIG. 16 shows a different illustrative embodiment in which display 111 f is partitioned into 5 regions: center region 1601 with 1 pixel per grid element, left and right regions 1602 and 1603 with 9 pixels per grid element each, and top and bottom regions 1604 and 1605, with 4 pixels per grid element each. In one or more embodiments grids and grid elements may not be rectangular. In one or more embodiments the highest resolution display region may not be located in the center of the display. In one or more embodiments the display regions may not be arranged symmetrically. In addition, center region 1601 may refer to the center of the screen or the center of the direction that an eye is pointing for eye tracking embodiments for example.

In one or more embodiments a renderer may generate values for the grid elements of the display regions. FIG. 17 illustrates an embodiment that includes 3D model 141, which is to be rendered onto display 111 g. The display comprises three regions: high resolution center region 1511 a, and low resolution side regions 1512 a and 1513 a. The renderer 1701 accesses the 3D model 141 and generates grid element values for the display. This renderer is a variable resolution renderer, because the grid elements in different regions of the display have different ratios of pixels per grid element. Area 1701 of 3D model 141 is projected to the display. Because the center region 1511 a has, for example, a single pixel per grid element, the objects rendered in the center are rendered with higher resolution than the objects rendered at the sides. For example, rendered grid element 1702 in region 1512 a comprises multiple pixels, and is therefore larger (lower resolution) than rendered grid element 1703 in region 1511 a, which comprises a single pixel.

In embodiments with grid elements that contain multiple pixels, the rendering process eventually sets the value of the individual pixels in the display. In the simplest case, the renderer sets the value of each pixel in a grid element to the rendered value of that grid element. FIG. 18 illustrates an embodiment in which the renderer 1701 renders a model onto grid 1801. (This grid corresponds to a display region. For illustration, each grid element in grid 1801 corresponds to 4 pixels in display region 1802. The rendering process 1803 for grid elements sets the grid element values for each grid element, such as for example value 1804 for grid element 1805. The grid element 1804 is illustrated as a list of attributes; in one or more embodiments grid element values may comprise any desired attributes, such as for example, without limitation, color, hue, saturation, value, intensity, texture, lighting, normal vector direction, material, transparency, or depth. In this example, the values 1804 of grid element 1805 are simply copied directly to the pixels within the grid element, such as pixel 1806. In one or more embodiments the assign pixel values process 1807 may copy or otherwise transform grid element values in any manner to set the values of the pixels within the grid elements. For example, in one or more embodiments the assign pixel values process may set only a subset of the pixels within a grid element to the value of the grid element. In one or more embodiments the assign pixel values process may calculate derived pixel values from the attributes of the grid element using any function, selection, relationship, or transformation.

One or more embodiments may perform additional optimizations to reduce rendering time and complexity, particularly in low resolution display regions. For example, one or more embodiments may include multiple geometry models at different levels of detail for objects in a 3D model. Level of detail techniques are typically used in the art to reduce level of detail for far away objects. One or more embodiments may extend this concept by using lower level of detail models for rendering onto low resolution display regions (with higher ratios of pixels per grid element), regardless of how far away an object is from the viewer. This use of multiple levels of detail for rendering independent of distance is not known in the art. FIG. 19 illustrates an embodiment with an object 1901 that appears in a 3D model. The embodiment obtains or generates two geometry models for the object 1901 at different levels of detail: geometry model 1902 is a high level of detail model, and geometry model 1903 is a low level of detail model. One or more embodiments may use any number of geometry models at different levels of detail for any object or objects in a 3D model. 3D model 141 b has three copies of this object, for illustration of how different levels of detail are used to render to different display regions. Object copy 1911 is rendered to center display region 1511 a. Because this region is a relatively high resolution region (with one pixel per grid element), the high level of detail model 1902 is used in this region. Object copy 1912 is rendered to left display region 1512 a. Because this region is a relatively low resolution region (with 4 pixels per grid element, for example), the low level of detail model 1903 is used in this region. Similarly, object copy 1913 is rendered to right display region 1513 a using low level of detail geometry model 1903. Rendering a low level of detail geometry model at a lower resolution provides additional savings in rendering time compared to an un-optimized renderer. One or more embodiments may select a level of detail geometry model for rendering based on multiple criteria, including for example a combination of object distance and the resolution of the display region being rendered to.

One or more embodiments may use any rendering techniques known in the art to render objects from a 3D model to the grids associated with display regions. FIG. 20 illustrates an embodiment that uses ray casting. As is known in the art, ray casting projects a ray from a viewpoint through each pixel, and uses attributes of the first object intersected by the ray to set the value of the pixel. Ray casting is often viewed as an inefficient rendering technique, because it requires computations for each pixel in a display. Because embodiments of the system may group pixels into grid elements, the number of rays required for ray casting may be significantly reduced. Ray casting may therefore provide an efficient rendering solution for one or more embodiments, particularly for rendering into low resolution display regions. In FIG. 20, ray 2001 is projected from viewpoint 2002 through grid element 2003 into 3D model 141 c. The nearest object in the 3D model intersected by ray 2001 is object 2004; thus the grid element value is set to reflect the attributes of this object 2004 at the point of intersection. In this illustrative example, a ray is not cast through each pixel, but is instead cast through each grid element (which may contain multiple pixels); this optimization improves rendering efficiency by reducing the number of rays required. In high resolution center display region 1511 a, grid elements contain (for example) single pixels, so ray casting in this region requires a relatively large number of rays. One or more embodiments may use a mixed rendering technique that for example uses ray casting to render in low resolution display regions but uses other techniques in high resolution display regions. One or more embodiments may use ray casting for all display regions. One or more embodiments may use ray tracing techniques, which consider secondary rays as well as primary rays.

A commonly used alternative to ray casting is rasterization, which effectively reverses the rays and projects from objects in the 3D model onto the display. One or more embodiments may use any of the rasterization rendering techniques known in the art. In addition, one or more embodiments may optimize rasterization techniques by rasterizing objects into grid elements, instead of into pixels as is typically performed in the art. Because there may be fewer grid elements than pixels, rendering efficiency may be improved by rasterizing to grid elements. FIG. 21 illustrates an embodiment of the system that uses grid element rasterization rendering. Geometric primitives (such as triangles for example) from object 2101 in 3D model 141 d are projected onto an image plane determined by the viewer's location and orientation. This projection yields a set of projected primitives in 2D space, which are then rasterized on a grid of grid elements. For example, the front face of object 2101 is rasterized onto grid 2102, which corresponds for example to a selected region of a display. Rasterization yields a list of grid element fragments 2103. Rasterization as known in the art generally results in a list of pixel fragments; one or more embodiments of the invention use grid element fragments instead of pixel fragments to improve rendering efficiency. In the example of FIG. 21, grid elements of grid 2102 contain 4 pixels each; thus rasterization efficiency may be improved for example by a factor of 4. Fragment table 2103 contains information about each grid element fragment. One or more embodiments may generate any desired grid element fragment information for each fragment. In the example shown, the grid element fragment information includes the x and y grid address of the grid element, the z-depth of the object (used during blending), and the color that should be assigned to the fragment. These values are illustrative; one or more embodiments may use other attributes for fragments as desired. Similarly, geometric primitives from object 2111 are projected onto an image plane and rasterized on grid 2102. This rasterization yields two additional grid element fragments 2112 and 2113. Grid element fragment blending process 2120 then combines the fragments to determine the values 2130 for each grid element. In this example, a simple blending using the z-buffer is used to discard the fragments 2112 and 2113. One or more embodiments may use any desired grid element fragment blending process to determine grid element values from the set of grid element fragments.

One or more embodiments may extend the grid element fragment information to include a list of pixels affected by or incorporated into the fragment. FIG. 22 illustrates the rasterization example of FIG. 21 with extended grid element fragment information. As in FIG. 21, the front face of object 2101 is projected to the image plane and rasterized on grid 2102. The resulting fragment table 2103 is expanded to include a list of pixels 2201 associated with each fragment. For example, grid element 2202 at grid address (5,0) includes 4 pixels with pixel addresses (10,0), (11,0), (10,1) and (11,1); these pixel addresses 2203 are associated with fragment #2 in the fragment table 2103. In this example, each grid element fragment lists all of the pixels associated with the grid element. However, one or more embodiments may associate a subset of the grid element pixels with a fragment, which may provide additional rendering flexibility and capability. In particular, one or more embodiments may render selected objects at a sub-grid element level, to increase the resolution of those selected objects.

FIG. 23 illustrates an embodiment of the system that renders selected objects designated as high resolution objects at a pixel-level resolution, even in low resolution display regions with multiple pixels per grid element. 3D model 141 e contains two objects: object 2301 is designated as a low resolution object, and object 2302 is designated as a high resolution object. One or more embodiments may classify any number of objects in a 3D model at any level of resolution. One or more embodiments may designate more than two levels of resolution for objects in a 3D model. High resolution objects may for example represent particular objects of interest that a user may focus on; hence it may be desirable to always render these objects at a high resolution, even in low resolution display regions. The renderer may generate grid element fragments that take into account whether a rendered object is a high resolution object or a low resolution object. For example, objects 2301 and 2302 are rasterized on grid 2303. Because object 2301 is a low resolution object, rasterization of this object completely fills each grid element associated with the object, such as grid element 2304. The fragment entry 2305 for this grid element therefore lists all of the pixels contained in the grid element. In contrast, because object 2302 is a high resolution object, rasterization of this object generates fragments that contain individual pixels. For example, fragment 2306 contains single pixel entry 2307, which corresponds to pixel 2308 within the associated grid element. Blending of fragments containing variable number of pixel entries is straightforward, since blending can be done for example on an individual pixel basis.

As described above with respect to FIG. 8A, one or more embodiments may use pose prediction to reduce the perceived latency between changes to the user's pose and corresponding changes to the display images shown to the user. Pose prediction may for example address the latency associated with image rendering by estimating where the user's pose will be at the time of display update, and by rendering (or rerendering) based on this predicted pose. As illustrated in FIG. 24, in one or more embodiments pose prediction may also be used in a two-stage process that first renders images using an initial predicted future pose, and then applies a rerendering approximation (such as warping) using a revised predicted future pose to correct for possible inaccuracies in the initial prediction. The architecture illustrated in FIG. 24 is an extension and refinement of the architecture illustrated in FIG. 2. Sensors 121 generate sensor data that measures the pose of the user, such as for example the pose of a headset attached to the user's head. Instead of using sensor data directly and immediately to calculate the user's current pose, sensor data may be provided to Pose Predictor 2401 to calculate a predicted future pose. In general, the Pose Predictor 2401 may estimate the future pose at any future point in time, based on current and past sensor data and based on any other relevant factors. Techniques for pose prediction are discussed below; FIG. 24 focuses on how the predicted pose information may be used to drive the rendering and image warping processes. In the architecture shown in FIG. 24, an initial phase of pose prediction uses for example sensor data 2411 (S₁), which may represent current or past sensor data at an initial time t₁ when an initial rendering of images begins. Pose Predictor 2401 generates initial pose prediction 2421 (Q_(f|1)) for the pose at a future time t_(f). As discussed further below, this future time may for example correspond to the time that the display images on the displays 111 are updated. The initial pose prediction 2421 is provided to Renderer 142, which generates 2D projections 160 and 161 based on this predicted pose. If the predicted pose 2421 corresponded exactly to the actual pose of the user at the time the displays 111 are updated, no additional corrections would be needed to the rendered projections 160 and 161. However, since the Pose Predictor 2401 may not make a completely accurate prediction, the user's actual pose at the time of display update may differ from the pose 2421 used by Renderer 142. Therefore, the embodiment illustrated in FIG. 24 applies a correction step as follows. After rendering, a second set of updated sensor data 2412 is obtained and is used to calculate a revised pose prediction 2422 (Q_(f|2)). This revised pose prediction 2422 is for the same future point in time as the initial pose prediction 2421. However, the revised prediction uses updated sensor data 2412, which is obtained at a point in time closer to the display update time; hence the revised pose prediction 2422 may be more accurate than the initial pose prediction 2421. (The revised prediction may also use any sensor data obtained prior to the latest sensor sample 2412; for example, the sensor may generate multiple samples between samples 2411 and 2412, and all of this data may be incorporated into the revised pose prediction 2422. In particular, the pose predictor may continuously integrate data from the sensors 121, for example to generate an updated estimate of the user's position, velocity, orientation, or angular velocity at any point in time whenever new sensor data is available.) Because there may not be sufficient time to perform an addition full rendering cycle by Renderer 142, the Image Warper 123 may be used to correct the 2D projections 160 and 161 using the revised predicted pose 2422. Specifically, the Image Warper may for example compare the initial predicted pose 2421 and the revised predicted pose 2422 to form a pose change 250, and may use this pose change to drive the rerendering approximation 260. This rerendering approximation may for example perform warping or other relatively efficient or rapid image corrections on images 160 and 161. In addition, in one or more embodiments the Image Warper may also apply additional image corrections, such as for example lens distortion correction 2402. Headsets used with displays 111 may for example incorporate one or more lenses, and these lenses may have imperfections or deliberately engineered features to bend light for various effects such as widening the user's field of view. Lens distortion correction 2402 may therefore modify warped images such as image 261 to account for these lens effects. The final corrected and warped images are then transmitted to displays 111.

FIG. 25 shows a flowchart with additional details on steps that may be used by one or more embodiments to implement the two stage prediction and correction process described above for FIG. 24. These steps are illustrative; one or more embodiments may implement prediction, correction, or both using additional steps or different steps, or by performing the steps of FIG. 25 in a different sequence. In this illustrative flowchart, two parallel and linked loops run continuously to provide animation of a 3D scene in a virtual reality display. Sensor Loop 2501 repeatedly samples and processes sensor data. Display Update Loop 2502 implements the prediction, rendering, and correction processes to generate display images. Display Update Loop 2502 uses the output from the Sensor Loop 2501. Specific steps that may be performed in one or more embodiments are as follows. Sample Sensors step 2511 obtains data from one or more of the sensors 121, such as for example, without limitation, an accelerometer, a gyroscope, or a magnetometer. In one or more embodiments different sensors may be sampled at different rates or at different times. Sensor Fusion step 2512 combines data from multiple sensors, for example to form an integrated estimate of a user's pose or of some aspect thereof. This fusion step may use techniques known in the art such as for example, without limitation, inertial integration algorithms, Kalman filters, or complementary filters. In step 2513 angular velocities measured in step 2511 or estimated in fusion step 2512 are recorded; this tracking of angular velocity history may be used for example for pose prediction, as discussed below. Motion Smoothing Filter step 2514 may apply any smoothing algorithm to the fused sensor data, for example to reduce the effects of noise, quantization, drift, or finite sampling rates.

Fused and smoothed sensor data available from step 2514 may be used at two points in the Display Update Loop 2502. Initially in step 2521 the current pose (or any data related to this pose) S₁ is obtained from the Sensor Loop at time t₁. This current pose (and possibly previous sensor data such as angular velocities recorded in step 2513) are used in Predict Future Pose step 2522, resulting in the initial predicted future pose Q_(f|1). Using this predicted future pose, step 2523 generates view matrices for each eye, for example for a stereo display. The view matrices are used in rendering step 2524 to generate rendered images corresponding to the initial predicted future pose Q_(f|1). In step 2525 the view matrices are stored for use in warping step 2534. Step 2526 is a delay, which we discuss below.

In the second stage of the Display Update Loop 2502, the rendered images generated in step 2524 are corrected to account for changes in pose since the initial prediction 2522. At step 2531 the current pose S₂ is obtained from the Sensor Loop at time t₂. In general, this pose S₂ may be different from the initial pose S₁, since the Sensor Loop 2501 may have executed one or more loops while the initial phase of the Display Update Loop 2502 occurred. Step 2532 generates a revised predicted future pose Q_(f|2) based on the updated sensor loop output S₂, and step 2533 generates updated view matrices based on this revised predicted future pose. In step 2534, the updated view matrices and the buffered view matrices from the initial phase are used to calculate warping matrices that apply the rerendering approximation to the rendered images generated in the initial phase. For example, if the left-eye view matrix from step 2523 during the first phase is V_(L)(t₁), and if the left-eye view matrix from step 2533 during the second phase is V_(L)(t₁), then a left-eye warp matrix from step 2534 may be calculated as W_(L)=V_(L)(t₁)⁻¹V_(L)(t₂). A right-eye warp matrix may be calculated in a similar manner as W_(R)=V_(R)(t₁)⁻¹V_(R)(t₂). The warp matrices may be combined in step 2535 with lens distortion corrections, and the combination of these corrections may be applied in step 2536 to warp the rendered images generated in step 2524. The warped images may then be displayed on the displays.

FIG. 26 shows an illustrative simplified swimlane timing diagram of selected steps from the flowchart of FIG. 25. At time 2601 (t₁), sensor data 2411 is obtained from sensor loop 2501, and is used in the initial pose prediction step 2522 to generate the initial predicted future pose 2421 (Q_(f|1)). The pose is predicted for future time 2603 (t_(f)) when the display 111 is updated. Since the display update process 2604 is typically not instantaneous, one or more embodiments may select a point during this process as the future time 2603; for example, in the embodiment shown in FIG. 26 the halfway point 2605 of the display update 2604 is used as the future time 2603. This halfway point 2605 may correspond for example to the point in time at which half of the pixels in the displays 111 have been updated. Rendering step 2524 generates rendered images based on the initial future predicted pose 2421. In some cases this rendering step may be relatively time-consuming. After rendering 2524, the second phase of the display update loop occurs. At time 2602 (t₂), sensor data 2412 is obtained and pose prediction 2532 generates a revised predicted future pose 2422 (Q_(f|2)). This revised predicted future pose 2422 is predicted for the same future time 2603 as the initial predicted future pose 2421, but it uses the updated sensor information 2412 to make the prediction. Image warping 2536 then occurs using the change in pose between 2421 and 2422, and the result of the image warping is displayed in display update step 2604.

FIG. 26 implicitly assumes that the display update step 2604 can occur immediately when the results of warping step 2536 are available. In many embodiments, display updates are performed on a regular update cycle, rather than immediately whenever new pixels are available. For example, one or more embodiments may update displays at a regular refresh rate such as 60 Hz or 120 Hz. FIG. 27 illustrates a modification to the timing diagram of FIG. 26 that takes into account these periodic display updates. For illustration, display updates occur at intervals 2701, such as every 1/60 of a second. If image warping step 2536 completes prior to the next scheduled display update, the system must wait for a time 2702 before sending the warped images to the display.

When a wait 2702 is required prior to display update 2604, the future pose prediction 2422 may be suboptimal since the actual pose may change substantially between measurement 2412 at time 2602 and display update time 2603. One of the purposes of the warping process 2536 is to perform a final correction to the rendered images using pose information that is as up to date as possible. Therefore one or more embodiments may use a modified process illustrated in FIG. 28 to perform this correction. In the illustrative timing shown in FIG. 28, the start time 2602 a (t′₂) of the second phase of the display update loop is purposely delayed so that it occurs “just in time” for the display update. Thus the wait time 2702 is effectively moved prior to the revised pose prediction as delay 2526. This delay pushes the sensor measurement 2412 and revised pose prediction 2422 closer to the display update time 2603, thereby improving the likely match between revised pose prediction 2422 and the actual pose at time 2603. In one or more embodiments an explicit delay may also be inserted prior to the initial sensor measurement 2411 and initial pose prediction step 2522 so that these are also performed “just in time” for display update 2603. In one or more embodiments delays may be inserted prior to either or both of the first phase of the display update loop (2411, 2522) and the second phase of the display update loop (2412, 2532).

We now discuss methods of pose prediction that may be used in one or more embodiments. Pose prediction may use any available information to estimate a future pose, including but not limited to current and past sensor data. FIG. 29 shows an illustrative embodiment that predicts a future pose by extrapolating current and past sensor data forward in time using for example numerical integration. This example focuses on the orientation component of pose; one or more embodiments may use similar methods for position prediction. Virtual reality headset 2900 has sensors 121, which for illustration include a rate gyroscope that measures angular velocity. Angular velocity ω is effectively a first derivative of orientation Q(t) through the differential equation dQ/dt=S(ω) Q, where S(ω) is the skew-symmetric cross-product matrix defined by S(ω)r=ω×r. Thus angular velocity can be effectively integrated using known methods to estimate future orientation. In one or more embodiments, the rate of change of angular velocity (angular acceleration α), which is effectively a second derivative of orientation, may also be used to predict future pose. In the embodiment shown in FIG. 29, current angular velocity 2911 is combined with previously measured angular velocity 2910 to form an estimate of current angular acceleration 2912. The current orientation 2901, current angular velocity 2911, and current angular acceleration 2912 are combined into state 2921. This state is used as initial conditions for the system 2922 of differential equations, which are numerically integrated from current time t₁ to the future time t_(f) for which the pose is predicted. The result of this integration is estimated future state 2923 at time t_(f), which includes predicted future orientation 2924. One or more embodiments may integrate only the first derivative 2911 and may not use the second derivative 2912. One or more embodiments may integrate system 2922 or similar differential equations using symbolic methods instead of or in addition to numerical methods. Numerical integration may use for example, without limitation, known techniques such as Euler's method or the Runge-Kutta method. One or more embodiments may use Runge-Kutta integration of any order, including but not limited to the typical fourth-order method.

One or more embodiments may incorporate models of the biomechanics of the user's body parts in predicting a future pose. For example, the head motion of a user is constrained because the neck cannot bend or twist to arbitrarily large angles. These constraints may be incorporated into pose prediction, as illustrated for example in FIG. 30. FIG. 30 shows upward and downward rotation of the head around a single horizontal axis for ease of illustration; in general constraints may be applied for motion of any body part along any number of axes or degrees of freedom. In the example shown in FIG. 30, the orientation of the head is measured using angle 3010 (θ) between the direction of the user's view and the horizontal plane. Constraint 3001 specifies a maximum value for this angle, and constraint 3002 specifies a minimum value for this angle. The current angle 3010, current angular velocity 3011, and current angular acceleration 3012 are input into numerical integration process 2922 a. This process also applies constraint 3020 that limits the angle to the range between 3002 and 3001.

Biomechanical models may also model forces and torques applied by or applied to body parts to aid future pose prediction. FIG. 31 expands on the example of FIG. 30 with a model of the torque applied by neck 3100 to resist bending upward or downward. Making for example a simple assumption that the torque is proportional to the angle of rotation (and in the direction that moves the neck back towards a neutral upright position), torque 3101 is proportional to angle 3010. This torque may be incorporated into the differential equations 2922 b modeling the time evolution of angle 3010, for example in equation 3102 that relates the angular acceleration to the torque. The angular acceleration also depends on the moment of inertia I of the head (and the headset) around the axis of rotation, which may be estimated or measured. This simple model 3101 of torque is illustrative; one or more embodiments may use any biomechanical model that incorporates any desired constraints, torques, forces, linkages, and degrees of freedom. For example, a variation on the model 3101 for torque may assume no resisting torque until the angle 3010 is close to the upper or lower limit of the range of motion, and then a steep rise in torque as these limits are approached more closely.

In one or more embodiments the system may incorporate elements that learn aspects of a biomechanical model that may be used for pose prediction. For example, the embodiment illustrated in FIG. 32 uses a device 3201, which may be for example a tablet computer or similar device, to prompt user 3202 to execute specific movements, and to record sensor values during these movements that may be used to construct a biomechanical model of the user's body. As an illustration, the system learns the upward and downward range of motion of the user's head as follows. First prompt 3211 tells the user to look up to the upper limit of the range. The device 3201 obtains sensor data from sensors 121 on the headset while the user responds to this prompt, and by analyzing this sensor data it determines the upper limit 3001. Then the device issues prompt 3212 telling the user to look down to the lower limit of the range, and measures sensor data to determine the lower limit 3002. In a similar manner one or more embodiments may learn any desired parameters of a biomechanical model. For example, without limitation, one or more embodiments may prompt users to move in any direction to the limits of the range of motion, to move at maximum possible or minimum possible speeds, to move by very small increments, or to move randomly in all directions. Analysis of sensor data collected during any or all of these motions may be used to construct a biomechanical model of the user's motion.

One or more embodiments may incorporate other information into pose prediction algorithms, including for example knowledge of regions of interest towards which the user may be inclined to look. FIG. 33 illustrates an example with a user viewing a scene 3301. The user is initially looking horizontally at ground level at the scene, in the direction 3302. However action occurs (or will occur soon) in the upper left area 3303 of the scene. This action may include for example motion of objects such as 3304, or sounds coming from specific locations such as sound 3305. The system may therefore model a tendency for the user to move his or her view towards the region of interest 3303. This model of an attractive force towards the region of interest may be combined with prediction based on current or past sensor data. For example, in the illustrative embodiment of FIG. 33, the system models an attractive “torque” 3310 that attempts to move the user's view angle towards the angle 3306 at which the region of interest lies. This torque is combined with data on the current angular velocity in differential equation model 2922 c. This model may be solved using numerical methods as described above, generating a predicted future pose 3312. One or more embodiments may determine one or more regions of interest in a scene based on any factors, including for example, without limitation, motion, speed, location of specific features or characters, sound, size of objects, or color of objects. One or more embodiments may determine regions of interest by learning what items users are attracted to when viewing a scene. One or more embodiments may create overlays such as menus, buttons, or other controls on a screen, and may treat the location of these overlays as possible regions of interest.

While the examples illustrated above generate a single predicted future pose at a point in time, in one or more embodiments the system may generate multiple predicted possible future poses for the same future point in time. Generating multiple predictions may be appropriate for example when there is significant uncertainty in the user's future motion. FIG. 34 illustrates an example where this uncertainty arises because a scene contains two different regions of interest. The user is initially looking towards the center of scene 3401. Two areas of action then appear in the scene, corresponding to region of interest 3303 at the upper left corner, and region of interest 3403 at the lower left corner. The corresponding angles 3306 and 3406, respectively, represent possible orientations for the user's head if the user looks at each region of interest. However, it may not be clear at the time of pose prediction which region of interest will draw the user's attention. Therefore the system may predict two different future poses, each corresponding to an assumption that one of the regions of interest draws the user's attention more strongly. Each of these two predicted future poses may be used for rendering in the first phase of the display update loop shown in FIG. 25. Specifically, at time t₁ when the initial pose predictions are made, pose 3411 corresponds to the scenario where the user looks towards region of interest 3303, and pose 3412 corresponds to the scenario where the user looks towards region of interest 3403. For each such pose, the system generates a corresponding set of rendered images: for example image 3421 corresponds to pose 3411, and image 3422 corresponds to pose 3412. In the second phase of the display update loop, the correction phase, the current pose is re-measured at time t₂ and is used to create a revised future predicted pose 3421. In this second phase, multiple future pose predictions are not made; instead the system makes a single best prediction in order to select an image for display. Based on the revised pose prediction 3421, the system determines which of the initial two pose predictions 3411 and 3412 were most accurate. For example, this selection may be based on determining which of the initial pose predictions was closest to the revised pose prediction 3421. In the example of FIG. 34, comparison 3422 shows that initial pose prediction 3411 was closer to revised pose prediction 3421. Therefore the system selects the corresponding rendered image 3421 and performs warping corrections on this image to generate a final image 3431 for display.

While the example shown in FIG. 34 generates two initial pose predictions (based on two regions of interest), one or more embodiments may generate any number of initial pose predictions based on any factors, and may render images based on each of these predicted poses. Image renderings for the various pose predictions may be generated in parallel if hardware and software support parallel rendering. In a second phase of the display update loop, one of the initial pose predictions may be selected based on any criteria, including but not limited to closeness to a revised pose prediction or to more recent sensor data, and the rendered images corresponding to this selected pose prediction may be corrected and displayed.

In one or more embodiments a rendered image may be partitioned into multiple tiles, and warping may be performed on each tile separately, potentially using different pose predictions for the warping of each tile. An illustrative embodiment that uses tiling is shown in FIG. 35. Rendering process 3501 generates image 3502, which is partitioned into three tiles 3511, 3512, and 3513. While FIG. 35 illustrates partitioning into three tiles, one or more embodiments may partition any or all of the rendered images into any number of tiles. Tiles may be of equal size and shape, or of different sizes or different shapes. Images may be tiled horizontally, vertically, both horizontally and vertically, or irregularly. Each of the tiles 3511, 3512, and 3513 is then warped (possibly after a delay) to correct the pose from the original pose prediction used for rendering 3501. Warping 3521 of tile 3511 generates warped tile 3531; warping 3522 of tile 3512 generates warped tile 3532; and warping 3523 of tile 3513 generates warped tile 3533. Each warped tile is then transmitted to one or more displays. For example, display update 3541 transfers warped tile 3531 to region 111 a of display 111; display update 3542 transfers warped tile 3532 to region 111 b of display 111; and display update 3543 transfers warped tile 3533 to region 111 c of display 111.

A potential advantage of partitioning a rendered image into tiles and warping each tile separately is that each tile may be warped using a pose prediction based on sensor data obtained just prior to the transmission of that tile to the display. This approach may reduce the lag between a user's change in pose and the corresponding change to a display image. FIG. 36 shows an illustrative timing chart for the steps shown in FIG. 35 to illustrate this effect of tiling. This timing chart is similar to the one shown in FIG. 28; however, the image is divided into tiles and each tile is warped separately as shown in FIG. 35. The timeline shows that the three tiles are sent to the display at different times. In this illustrative example, the midpoint of each tile update cycle is used as the prediction time for warping. For example, the display update for tile 1 is halfway complete at time 3601 (t₂₁); the display update for tile 2 is halfway complete at time 3602 (t₂₂); and the display update for tile 3 is halfway complete at time 3603 (t₂₃). Because the overall display update is halfway complete at time 3602, this time is used for the initial pose prediction 3610 that is used for rendering 3501. After rendering 3501, a delay 3605 may be inserted so that warping is performed just in time, as described with respect to FIG. 28. In one or more embodiments there may be no delay, and warping may follow rendering directly. As shown in FIG. 35, warping is then performed on each individual tile of the rendered image. One or more embodiments may use any desired future point in time to make a revised pose prediction for the warping of each tile. In the embodiment shown in FIG. 36, the midpoint of each tile's update on the display is used for the pose prediction for that tile. Thus for example pose prediction 3611 predicts the pose at time 3601, which is used for warping 3521 of tile 1; pose prediction 3612 predicts the pose at time 3602, which is used for warping 3522 of tile 2; and pose prediction 3613 predicts the pose at time 3603, which is used for warping 3523 of tile 3. Each of the pose predictions 3611, 3612, and 3613 may for example use the most recent sensor data that is available at the time the pose prediction is performed. By using the most recent sensor data, and by predicting the pose at a time that corresponds to the update of each individual tile on the display, the timing illustrated in FIG. 36 may minimize lags between pose changes and display updates that reflect these changes.

While the invention herein disclosed has been described by means of specific embodiments and applications thereof, numerous modifications and variations could be made thereto by those skilled in the art without departing from the scope of the invention set forth in the claims. 

What is claimed is:
 1. A predictive virtual reality display system with post rendering correction, comprising at least one display viewable by a user; at least one sensor that generates sensor data that measures one or more aspects of a pose of one or more body parts of said user; a pose predictor coupled to said at least one sensor, wherein said pose predictor is configured to calculate a predicted pose of said one or more body parts of said user at a future point in time, based on said sensor data generated by said at least one sensor and based on said future point in time; a 3D model of a scene; a scene renderer coupled to said at least one display, said pose predictor, and said 3D model, wherein said scene renderer is configured to obtain an initial predicted pose at a future display update point in time from said pose predictor; calculate one or more 2D projections of said 3D model, based on said initial predicted pose; an image warper coupled to said at least one display, said scene renderer, and said pose predictor, wherein said image warper is configured to receive said initial predicted pose at said future display update point in time from said scene renderer; receive said one or more 2D projections from said scene renderer; obtain a revised predicted pose at said future display update point in time from said pose predictor; calculate a change in pose between said initial predicted pose and said revised predicted pose; generate a rerendering approximation of said one or more 2D projections based on said change in pose; modify said one or more 2D projections based on said rerendering approximation; transmit said one or more 2D projections to said at least one display.
 2. The system of claim 1 wherein said one or more body parts of said user comprise a head of said user.
 3. The system of claim 1 wherein said modify said one or more 2D projections is further based on a lens distortion correction.
 4. The system of claim 1 wherein said future display update point in time is a halfway point in a pixel update cycle of said at least one display.
 5. The system of claim 1 wherein said image warper is further configured to delay said obtain said revised predicted pose until a just in time pose revision time, wherein said just in time pose revision time is a latest time that allows said transmit said one or more 2D projections to said at least one display to occur by said future display update point in time.
 6. The system of claim 1 wherein said occur by said future display update point in time comprises update half of pixels of said at least one display by said future display update point in time.
 7. The system of claim 1 wherein said calculate a predicted pose of said one or more body parts of said user at a future point in time comprises calculate a current pose from said sensor data; calculate one or more derivatives of pose from said sensor data; extrapolate said current pose and said one or more derivatives of pose to said future point in time to form said predicted pose.
 8. The system of claim 7 wherein said at least one sensor comprises a rate gyroscope; said calculate said one or more derivatives of pose comprises obtain an angular velocity from said rate gyroscope; set a first derivative of pose of said one or more derivatives to said angular velocity.
 9. The system of claim 7 wherein said extrapolate said current pose and said one or more derivatives of pose comprises apply one or both of an Euler's method or a Runge-Kutta method to numerically integrate said one or more derivatives of pose.
 10. The system of claim 1 wherein said pose predictor is further coupled to a body model of said one or more body parts of said user; said calculate a predicted pose of said one or more body parts of said user at a future point in time is further based on said body model.
 11. The system of claim 10 wherein said body model comprises one or more of a limit on a range of motion of said one or more body parts; and, a model of forces applied by or applied to said one or more body parts.
 12. The system of claim 10 further comprising a body model generator that is configured to analyze said sensor data to create said body model.
 13. The system of claim 12 wherein said body model generator is further configured to prompt said user to execute one or more movements; analyze said sensor data during said one or more movements to create said body model.
 14. The system of claim 1 wherein said pose predictor is further configured to receive a region of interest; calculate said predicted pose of said one or more body parts of said user at said future point in time, based on said sensor data generated by said at least one sensor; said future point in time; and, said region of interest.
 15. The system of claim 1 wherein said region of interest is based on one or more of a location in said 3D model with action; a location in said 3D model that emits a sound; a location of a menu item on said at least one display or in said 3D model.
 16. The system of claim 1 wherein said pose predictor is further configured to calculate a plurality of predicted poses of said one or more body parts of said user at said future point in time.
 17. The system of claim 16 wherein said scene renderer is configured to receive a plurality of initial predicted poses at said future display update point in time from said pose predictor; calculate one or more 2D projections of said 3D model for each initial predicted pose of said plurality of initial predicted poses; said image warper is configured to receive said plurality of initial predicted poses at said future display update point in time from said pose predictor; receive said one or more 2D projections of said 3D model for each initial predicted pose of said plurality of initial predicted poses from said scene renderer; obtain said revised predicted pose at said future display update point in time from said pose predictor; select a closest initial predicted pose from said plurality of initial predicted poses that best matches said revised predicted pose; select one or more closest 2D projections that were rendered based on said closest initial predicted pose; calculate a change in pose between said closest initial predicted pose and said revised predicted pose; generate a rerendering approximation of said one or more closest 2D projections based on said change in pose; and modify said one or more closest 2D projections based on said rerendering approximation; transmit said one or more closest 2D projections to said at least one display.
 18. The system of claim 1 wherein said image warper is further configured to partition at least one of said one or more 2D projections into a plurality of tiles; for each tile of said plurality of tiles determine a tile display update point in time as a halfway point in a pixel update cycle of said tile on at least one display; obtain a tile revised predicted pose at said tile display update point in time from said pose predictor; calculate a tile change in pose between said initial predicted pose and said tile revised predicted pose; generate a rerendering approximation of said tile based on said tile change in pose; modify said tile based on said rerendering approximation of said tile; transmit said tile to said at least one display. 